Amir Hazem

Mériem Bouhandi

Florian Boudin

Béatrice Daille

Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data

Keywords: ATE, automatic term extraction, terminology, low resource, multilingual

Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and an important basis for further natural language processing applications. Even with persistent improvements, ATE still exhibits weak results exacerbated by small training data inherent to specialized domain corpora. Recently, transformers-based deep neural models, such as BERT, have proven to be efficient in many downstream NLP tasks. However, no systematic evaluation of ATE has been conducted so far. In this paper, we run an extensive study on fine-tuning pre-trained BERT models for ATE. We propose strategies that empirically show BERT's effectiveness using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. Experiments have been conducted on four specialized domains in three languages. The obtained results suggest that BERT can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE.

Introduction

Automatic Term Extraction (ATE) is essential to specialized domains knowledge understanding and can further be used as input for other NLP applications, including information retrieval, thesauri construction, machine, and computer-aided translation, etc. The aim of ATE is to identify terminological units in specific domains [START_REF] Castellví | Automatic term detection: A review of current systems[END_REF]. For that purpose, several methods have been proposed, ranging from linguistic-based [START_REF] Ananiadou | A methodology for automatic term recognition[END_REF][START_REF] Justeson | Technical terminology: some linguistic properties and an algorithm for identification in text[END_REF], statistical [START_REF] Schäfer | Evaluating noise reduction strategies for terminology extraction[END_REF], feature-based [START_REF] Conrado | A machine learning approach to automatic term extrac-tion using a rich feature set[END_REF] as well as hybrid [START_REF] Basili | Inducing terminology for lexical acquisition[END_REF][START_REF] Aubin | Improving term extraction with terminological resources[END_REF] and deep neural-based (Wang et al., 2016;[START_REF] Hätty | Finegrained termhood prediction for German compound terms using neural networks[END_REF] approaches. Nonetheless, they still exhibit weak results, and manual post-filtering remains necessary to exploit current system outputs (Terryn et al., 2018). Furthermore, featurebased approaches, which, up to now, are considered among the most accurate, require labor-intensive feature engineering with no straightforward transfer learning if a new domain is addressed. The only transfer learningbased method that we are aware of deals with clinical concept detection, and still requires feature engineering [START_REF] Lv | Transfer learning based clinical concept extraction on data from multiple sources[END_REF]. Current research on ATE deals with two main problems: (1) the small size of data collections inherent to specialized domains and (2) the limited availability of annotations due to the lack of consensus about the nature of terms (Terryn et al., 2018). The former problem has been addressed in other downstream applications using data selection or data augmentation, as shown in SMT [START_REF] Moore | Intelligent selection of language model training data[END_REF][START_REF] Axelrod | Domain adaptation via pseudo in-domain data selection[END_REF][START_REF] Gascó | Does more data always yield better translations?[END_REF], and NMT [START_REF] Parcheta | Data selection for nmt using infrequent ngram recovery[END_REF] as well as combining specialized and general domain word embeddings as demonstrated in the clinical concept detection [START_REF] El Boukkouri | Embedding strategies for specialized domains: Application to clinical entity recognition[END_REF] and bilingual terminology extraction [START_REF] Liu | Towards a unified framework for bilingual extraction of single-word and multi-word terms[END_REF] tasks. Nonetheless, these techniques are often difficult to apply for ATE since they all require a reasonable amount of annotated data1 . The latter problem has also been addressed in other applications using transfer learning, such as, in conversational AI [START_REF] Schuster | Cross-lingual transfer learning for multilingual task oriented dialog[END_REF] and named entity recognition [START_REF] Jia | Cross-domain NER using cross-domain language modeling[END_REF][START_REF] Rahimi | Massively multilingual transfer for NER[END_REF] tasks. However, transfer learning is based on mapped representations learned from one sufficient annotated source data and targeted another less-resourced data. In ATE, proper and sufficient source annotations are not available, making such transfer learning difficult even to consider. To overcome the annotation gap occurring in ATE, [START_REF] Judea | Unsupervised training set generation for automatic acquisition of technical terminology in patents[END_REF] proposed an unsupervised data generation approach. However, it relies on linguistic features that need to be defined beforehand. Over the last few years, a new family of deep neural approaches impulsed by transformers has brought forward significant improvements in many downstream applications (Vaswani et al., 2017;[START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF][START_REF] Raffel | Exploring the limits of transfer learning with a unified text-to-text transformer[END_REF]. BERT, for instance, can learn bidirectional context representations from a considerable amount of general domain data that can be fine-tuned for a specific task. These approaches remarkably capture semantic and syntactic information [START_REF] Rogers | A primer in bertology: What we know about how bert works[END_REF][START_REF] Reif | Visualizing and measuring the geometry of bert[END_REF], and supersede classical approaches on many NLP tasks. Based on the assumption that terms share terminologically-marked contexts [START_REF] Meyer | Extracting knowledge-rich contexts for terminography: A conceptual and methodological framework[END_REF], we put greater stress on this hypothesis and propose to investigate its validity on cross-domain and cross-lingual scenarios by experiencing the usage of BERT for ATE on four specialized domains in three different languages. We believe our work provides three important contribu-tions: 1) The first extensive study of BERT for ATE; 2) The proposition of efficient and straightforward BERTbased strategies (as a binary classifier, as a sequence labeling method, and as features for a BiLSTM-CRF) that can be further improved by post-filtering; 3) The empirical verification that the terminologically-marked contexts assumption holds in both the cross-domain and cross-lingual scenarios thanks to transfer learning.

Related Work

Several approaches for ATE have been proposed so far, ranging from the early linguistic-based [START_REF] Bourigault | Surface grammatical analysis for the extraction of terminological noun phrases[END_REF][START_REF] Justeson | Technical terminology: some linguistic properties and an algorithm for identification in text[END_REF] to the recent deep neuralbased (Wang et al., 2016;[START_REF] Hätty | Finegrained termhood prediction for German compound terms using neural networks[END_REF]. We divide their classification into supervised versus unsupervised methods.

Unsupervised Methods

Unsupervised methods usually involve two steps: (i) the identification of term-like units and (ii) the filtering and sorting of the extracted units that may not be terms using several rules [START_REF] Daille | Term variation in specialised corpora: characterisation, automatic discovery and applications[END_REF]. The term-like units are often sequences of noun chunks that are selected during the identification step according to their part-of-speech and their morpho-syntactic patterns [START_REF] Justeson | Technical terminology: some linguistic properties and an algorithm for identification in text[END_REF], as well as general phrase rules involving phrase borders [START_REF] Bourigault | Surface grammatical analysis for the extraction of terminological noun phrases[END_REF], samples of annotated texts and lists of seed terms [START_REF] Jacquemin | Spotting and Discovering Terms through Natural Language Processing[END_REF]. [START_REF] Bourigault | Surface grammatical analysis for the extraction of terminological noun phrases[END_REF], for instance, introduced a two steps-based method: (1) analysis: in which a set of rules of frontier marker identification (e.g., punctuation marks, verbs, pronouns, etc.) is defined and (2) parsing: in which the maximal-length noun phrases are determined, and sub-group candidates are extracted based on grammatical structure rules. [START_REF] Justeson | Technical terminology: some linguistic properties and an algorithm for identification in text[END_REF] used the linguistic properties of technical terminology. Terms can also be collected using the lexical expansion of seed term lists and nominal phrases that include seed terms [START_REF] Jacquemin | Spotting and Discovering Terms through Natural Language Processing[END_REF][START_REF] Burgos Herera | Towards an Image-Term Co-occurrence Model for Multilingual Terminolog Alignment and Cross-Language Image Indexing[END_REF]. In the filtering step, all the collected term-like sequences are usually ranked according to termhood and unithood [START_REF] Kageura | Methods of automatic term recognition: a review[END_REF] criteria, which can be measured by frequency, association measures (e.g., mutual information, log-likelihood), specificity measures (Ahmad et al., 1994), etc. Filtering can also be done by removing nested sequences (incorrect, incomplete, or expansions that are not part of the base term). This is usually done using several measures, such as the Cvalue [START_REF] Frantzi | The C-Value/NC-Value domain independent method for multi-word term extractionAlgorithmsBased on Error Counting[END_REF], contextual filtering under the assumption that terms are gregarious, the NC-value [START_REF] Frantzi | The C-Value/NC-Value domain independent method for multi-word term extractionAlgorithmsBased on Error Counting[END_REF] or by exploiting the fact that terms, contrary to regular noun phrases, share terminologically-marked contexts [START_REF] Meyer | Extracting knowledge-rich contexts for terminography: A conceptual and methodological framework[END_REF][START_REF] Condamines | Corpus analysis and conceptual relation patterns[END_REF]. In this work, we assume that terms do share strongly marked contexts that BERT is able to capture, and we push this hypothesis further to crossdomain and cross-lingual scenarios.

Supervised Methods

Prior works on ATE used different learning algorithms and feature sets [START_REF] Conrado | A machine learning approach to automatic term extrac-tion using a rich feature set[END_REF][START_REF] Judea | Unsupervised training set generation for automatic acquisition of technical terminology in patents[END_REF][START_REF] Hätty | Evaluating the reliability and interaction of recursively used feature classes for terminology extraction[END_REF]. [START_REF] Hatzivassiloglou | Disambiguating proteins, genes, and rna in text: a machine learning approach[END_REF], for instance, presented an automated system for assigning protein and gene class labels to biological terms in free texts. They explored three established learning techniques: Bayesian learning [START_REF] Duda | Pattern classification and scene analysis[END_REF], decision tree using C4.5 implementation [START_REF] Quinlan | C4.5: Programs for Machine Learning[END_REF], and inductive rule learning using RIPPER implementation [START_REF] Cohen | Learning trees and rules with setvalued features[END_REF]. [START_REF] Takeuchi | Bio-medical entity extraction using support vector machines[END_REF] used support vector machines to extract Bio-medical entities, while Zhang and Fang (2010) used Conditional Random Fields (CRF) and syntactic features. These methods, among others, require quite a high number of features to be set. [START_REF] Conrado | A machine learning approach to automatic term extrac-tion using a rich feature set[END_REF], for instance, explored Naive Bayes and decision tree using 17 linguistic, statistical, and hybrid features, such as frequency, TF-IDF, POStags, etc. They also used two additional features from corpora that belong to another domain Finally, [START_REF] Hätty | Evaluating the reliability and interaction of recursively used feature classes for terminology extraction[END_REF] used a random forest classifier, trained on several termhood measures, and recursively applied to the components. To overcome the lack of training data, [START_REF] Judea | Unsupervised training set generation for automatic acquisition of technical terminology in patents[END_REF] proposed an unsupervised training data augmentation step prior to using a binary classifier and a CRF trained on different types of features. Even if their approach is languageindependent, it has only been tested on English and still relies on linguistic features that need to be defined beforehand. More recently, several deep neural approaches have also been applied to ATE. Wang et al. (2016) used two deep neural classifiers: a CNN that learns terms representation using multiple filters to capture sub-gram information and an LSTM that captures the meaning of a term from the sequential combination of its constituents. [START_REF] Amjadian | Local-global vectors to improve unigram terminology extraction[END_REF] leveraged local and global embeddings to encapsulate the meaning of the term for the classification step, although they only work with unigram terms. Few neural-based methods regard ATE as a sequence labeling task [START_REF] Han | Cnn-bilstm-crf model for term extraction in chinese corpus[END_REF]. In clinical concept detection, which can be seen as a sub-task of ATE, several methods have, however, been proposed [START_REF] Lv | Transfer learning based clinical concept extraction on data from multiple sources[END_REF][START_REF] El Boukkouri | Embedding strategies for specialized domains: Application to clinical entity recognition[END_REF]. Finally, Šajatović et al. (2019) evaluated 16 state-of-the-art methods at the corpus and document level, showing that there is no best-performing single method for corpus-level ATE.

BERT for ATE

We address ATE by introducing three different strategies. The first consists in using BERT as a binary classifier, while the second uses BERT as a sequence labeling model for Named Entity Recognition (NER). Finally, we propose a feature-based strategy which consists in using BERT embeddings as input features for a bidirectional LSTM (biLSTM) with a Conditional Random Fields (CRF) layer.

BERT as Binary Classifier

BERT is a supervised learning model2 that has been trained on the Masked Language Model (MLM) objective and Next Sentence Prediction (NSP). For NSP, the model takes as input pairs of sentences and learns to predict if the second sentence is the subsequent sentence of the first one. We similarly fine-tune our model, providing sentence/term pairs and sentence/non-term pairs instead of sentence pairs. In a similar fashion to NSP, we select 50% of the training pairs where the second sequence is a term, while the other 50% pairs consist of randomly chosen n-grams3 . Therefore, for each positive training pair, we randomly select within the sentence of the same pair an n-gram as non-term to keep a balanced training set. Class balancing is often a vital setup for classifiers' efficiency. Even if some existing techniques do reduce the impact of unbalanced classes, we only deal with balanced training in this work. The intuition behind this step is that BERT will learn shared features between terms and their contexts the same way it does for subsequent pairs of sentences. Moreover, for each correct input pair, the term is present in both sequences, which we expect to be advantageous to learn more context about terms during the masked LM phase. Upon testing, all the n-grams of a sentence are used to provide sentence/term-like pairs.

BERT as NER system

We also address ATE as a named entity recognition task where each term is seen as a named entity. Therefore, we can easily adapt the pre-trained BERT for NER in order to perform ATE. This procedure can be seen as an over-simplification of the actual NER task where we consider only one named entity type (a term), whereas, in the classical task, we consider several named entities. Similarly to NER, we use the IOB scheme (I indicates that the token is inside an entity, O indicates that a token is outside the entity, and B indicates that the token begins the entity). Since the BERT NER is already pre-trained following the IOB scheme, we chose the ORG tag for all the experiments4 . Hence, each term of a given sentence is assigned by B-ORG or I-ORG labels, and all non-terms are assigned by O-ORG.

Bi-LSTM for Sequence Labeling with BERT Embeddings

We finally address ATE as sequence labeling task. Bidirectional LSTM (biLSTM) are standard to the task of named entity recognition using sequence labeling.

Data Sets

Up to now, cross-lingual and cross-domain transfer methods for ATE have been hindered by the lack of multilingual data sets annotated according to the same guidelines [START_REF] Schuster | Cross-lingual transfer learning for multilingual task oriented dialog[END_REF]. However, the recently released data set from the first TermEval shared task [START_REF] Rigouts Terryn | Termeval 2020: Shared task on automatic term extraction using the annotated corpora for term extraction research (acter) dataset[END_REF] offers an appropriate annotation methodology that falls within both multilingual and multi-domain scenarios. Therefore, in our experiments and evaluation, we use the TermEval data sets, which consist of four specialized domains: corruption, dressage, wind energy, and heart failure in three languages: English (en), French (fr), and Dutch (nl). Table 1 depicts the number of documents and tokens for each corpus as well as the test lists size.

Experiments and Results

We follow the evaluation procedure defined in the Ter-mEval shared task (Rigouts Terryn et al., 2020) which consists in using three domains (corruption, wind energy, and dressage) for training and validation and the heart failure domain for testing. We first report the results on the development sets used for cross-validation and fine-tuning, and then the obtained results on the heart failure test set. For each experiment, reported results are the mean average of 10 runs.

Pre-trained Models

As pre-trained models can behave differently according to the addressed tasks [START_REF] Liu | Roberta: A robustly optimized bert pretraining approach[END_REF], we evaluate several BERT models that we have fine-tuned on Table 2: ATE scores (%) in terms of Precision (P), Recall (R) and F1-score (F1). For each validation corpus, the two remaining ones were used for fine-tuning. We contrast 3 usages of BERT : as a binary classifier, BERT-NER and BERT embeddings used with a biLSTM-CRF. We also contrast BERT's multi-lingual version as well as RoBERTa for English and Camembert for French.

the specialized corpora. The tested models for English were: bert-base-cased (BERT) [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF], bert-base-multilingual-cased (BERT-multi), and robertabase (RoBERTa) [START_REF] Liu | Roberta: A robustly optimized bert pretraining approach[END_REF] 5 . For French, we used Camembert [START_REF] Martin | CamemBERT: a Tasty French Language Model[END_REF] and bert-basemultilingual-cased (BERT-multi). Finally, for Dutch, we only considered bert-base-multilingual-cased.

Dev Set Experiments

Domain Transfer Learning Evaluation

The domain transfer learning procedure consists of finetuning BERT on a given domain and testing on another domain. Having three validation data sets (corruption (Corp), dressage (Equi), and wind energy (Wind)), and several BERT models, we subdivided the domain transfer evaluation into two experiments: 1) fixing the training data sets while varying BERT models and 2) fixing the models while varying the training data sets. Table 2 reports the obtained results when varying BERT models using BERT as a binary classifier, BERT as NER, and BERT with biLSTM-CRF. The contrasted models are BERT and its multilingual version (BERTmulti), as well as RoBERTa for English and Camem-BERT for French. For each specialized test set, we fine-tuned on the combination of the remaining data sets, which consists of providing BERT with both domains as one single data set. If this procedure may seem counter-intuitive, we expect that BERT can capture cor-relations between terms across domains by assuming that they have terminologically-marked contexts.

Overall, better results were obtained in terms of the F1 score when using BERT as a binary classifier. However, BERT (NER) showed competitive results and was sometimes better for Dutch and always better in terms of precision (P). When comparing BERT models used for binary classification, we see that the results are more contrasted for English and that no single model performs the best in each domain. For French, it is clear that Camembert is the best performing model in terms of the F1 score. When it comes to BERT (NER), we observe in some cases a considerable drop in the results for BERT-multi (NER) and Roberta (NER) for the Corp domain. This drop could be linked to the lower number of named entities in the Corp data set compared to the other corpora. Conversely, BERT-multi (NER) outperformed BERT-multi for Dutch on Equi and Wind test sets. Finally, we observe that BERT-biLSTM-CRF obtained way lower results, suggesting that using BERT embeddings as features for a biLSTM-CRF is less effective than using BERT as a classifier. This is not surprising as we already know that biLSTM models often require a massive amount of training data, which is definitely not the case in our data sets.

Based on the previous experiment's best performing models, we report in obtained for BERT (NER) and BERT-biLSTM-CRF when using both Equi and Wind corpora. However, these observations do not hold for all the experiments and vary depending on the chosen data sets and the used models. Overall, contrasting the results suggest that transfer learning for ATE is domain sensitive and that a careful choice of training data sets is undoubtedly needed for better performance.

Language Transfer Evaluation

The language transfer experiment, shown in Table 4, questions the usefulness of adding other languages for fine-tuning. Hence, given the English corruption test set for instance (annotated as corp (en)), we fine-tune the multilingual BERT model (BERT-multi) on each of the two remaining data sets separately (equi or wind) as well as their combination (noted as All), which simultaneously represent cross-lingual and cross-domain evaluation. As can be seen, adding other languages often increases the results, and when it is not the case, the F1 score difference remains very low. Improvements are more noticeable for the Dutch test sets, where we obtained an F1 score of 41.56% versus F1 of 37.52% without the use of multiple languages when compared to a fine-tuning using the Corp data set only. Nonetheless, we notice that, in some cases, adding more languages decreases the performance, as it can be noticed when testing on Wind (en) and fine-tuning on Corp. Indeed, the F1 score dropped from 34.04% to 32.89% when adding French and Dutch languages to English (en+fr+nl). Finally, some improvements can also be observed when using both cross-lingual and cross-domain combination.

Test Set Experiments

Baselines

In contrast to our proposed approaches, we implemented a feature-based baseline that exploits eXtreme Gradient Boosting (XGBoost) [START_REF] Chen | Xgboost: A scalable tree boosting system[END_REF] for classification6 . We also implemented a Vanilla-biLSTM- [START_REF] Rigouts Terryn | Termeval 2020: Shared task on automatic term extraction using the annotated corpora for term extraction research (acter) dataset[END_REF]. The teams performed various approaches: e-Termino used a rule-based approach, RACAI used a combination of statistical approaches based on several features, NLPLab used a biLSTM based on pre-trained Glove and NYU used chunking, statistical and search-based scores. Finally, the best performing team (TALN-LS2N) used BERT as a binary classifier which is similar to our approach from a data representation perspective but differs from it did not exploit cross-lingual transfer, and 2) they only performed a combination of all training data for finetuning. On the contrary, we investigate several domain combinations and show that more cross-domain data does not necessarily lead to better performance.

Results

Table 5 shows the obtained results on the heart failure test set. We first report the results for our cross-domain feature-based approach, noted Features (en) for English and its cross-lingual version, noted Features (enfrnl) for all languages as well as our Vanilla-biLSTM-CRF baseline. We also list the official results of the shared task for all the participating teams. Finally, we report the results of our proposed approaches using BERT, BERT (NER), as well as BERT-biLSTM-CRF.

Overall, our achieved results on the test sets follow the observed results obtained on the development sets. Indeed, BERT for English, CamemBERT for French, and BERT-multi for Dutch obtained the best F1 scores, while BERT (NER) obtained the best precision. Our methods outperformed the baselines as well as the participating teams with a large margin for Dutch. Using cross-lingual transfer learning did not improve the results compared to BERT-multi (en) and BERT-multi (enfrnl). It also noticeably weakened the results for French. This can also be observed for the Features (enfrnl) baseline, where the results drop significantly. Finally, even if the BERT-BiLSTM-CRF is behind the classifier based-BERT model, it shows, however, noticeably better results than the Vanilla-biLSTM CRF, which highlights the substantial contribution of BERT embeddings as input features for the biLSTM for this task. Table 7: Results after filtering down our best models output lists using different filters, improving the results. For the pattern filtering, all terms that fit nominal patterns are kept. For the C-Value and the Specificity, we only keep the first half of the ordered filtered list.

Analysis and Discussion

carried out directly on the terms, but we could go a step further by selecting a finer granularity (specific, common, out-off-domain, named entity). However, these classes were extremely unbalanced. While it was not the concern of this work, future work will adapt the models to exploiting each class's particularities. Finally, many classic ATE approaches apply post-filtering to the collected term-like sequences. In the same way, we applied to our models outputs three main automatic filtering techniques that are: pattern filtering where all terms that fit nominal patterns were kept; C-Value and the Specificity measures where only the first half of the ordered lists were kept. Table 7 reports the obtained results of each filtering technique. We first see that pattern filtering did not improve the results, suggesting that BERT's outputs are morpho-syntactically correct. We also see that C-Value filtering significantly improves the results for each model, while Specificity filtering F1 scores are less important for French and drastically weaker for Dutch. However, for English, the F1 is improved by 7.47%.

Among the three proposed BERT-based approaches, the binary classification system obtained the best results.

Varying the data sets revealed the effectiveness of crossdomain BERT fine-tuning and lent support the assumption that terms share cross-domain marked-contexts captured by BERT. This finding opens a new interesting line of research for ATE since the lack of training data can be reduced by using other specialized domains. Nonetheless, the results also revealed that using several training data sets does not necessarily guarantee better performance. We still have an unanswered question: how to choose the most appropriate training data sets? Regardless of the empirical improvements, it is worth highlight-ing the simplicity of our strategies, since neither feature engineering nor pattern design is needed. Besides that, observing BERT outputs underlined the presence of some inappropriate or ungrammatical term-like candidates. Post-filtering techniques proved to be remarkably helpful for improving the results.

Our second strategy considered ATE as a sequence labeling problem. While named entities are present in all corpora, we expected better performance from BERT (NER). However, only a small proportion of named entities was extracted 10 . Overall, and despite lower F1 scores, BERT (NER) showed acceptable and sometimes competitive results compared to other methods. It also obtained the best precision over all the experiments. This is more noticeable for Dutch BERT (NER) as it outperformed the BERT classifier. Finally, the larger proportion of extracted specific terms can be explained by their more extensive representation in the training sets or by the fact that they may share more definite marked contexts. We show that BERT can be used for ATE, replacing classic methods to identify term-like units more accurately, and it can be followed by standard post-filtering techniques, further improving its results.

Conclusion

In this paper, we proposed the first systematic study of BERT models for the ATE task on four low resource specialized domains in three languages. We experimented with BERT as a binary classifier, and as a named entity recognition system, as well as a biLSTM-CRF trained on BERT features. The obtained empirical results indicate that BERT is able to transfer learning across do-

Appendix

Additional information is presented in this supplementary material. We first present models settings and the detailed statistics of the evaluation term lists of each domain and for each language. Then, we present additional BERT models evaluations through figures and extended tables.

9.1. Settings 9.1.1. BERT Settings For the fine-tuning phase of BERT, we used the simpletransformers 11 library and its default parameters setting.

For BERT as a binary classifier, we used only 1 epoch for fine-tuning, while for BERT as NER task, we used 4 epochs. Nonetheless, no significant impact on the results has been observed when using more epochs.

BiLSTM-CRF Hyperparameters

As for our biLSTM-CRF, we choose 150 hidden units for the BiLSTM, and 100 in the ReLU activated hidden layer, depending on the validation set performance. In a similar fashion to others who used word embeddings like GloVe [START_REF] Pennington | Glove: Global vectors for word representation[END_REF] or fastText [START_REF] Bojanowski | Enriching word vectors with subword information[END_REF] as their input, we use the last layer of pre-trained bert-base-cased (EN), CamemBERT (FR) and bert-base-multilingual-cased-dutch (NL) as BERT embeddings. The model was built using Keras and trained with a batch size of 64, for 10 epochs.

Results

In this section, we provide extensive and more comprehensive tables and experiments that we were unable to add to the main paper due to a lack of space. In Table 9, we show ATE scores for each validation corpus. In addition to the BERT models presented in the paper, we also contrast our results with extra models. Tables 10 and11 show cross-domain and cross-lingual results in more details, on the validation sets as well as on the test set. Table 1 shows for each term type in the output list, its proportion in the gold standard, and corpus used for training for the heart failure English test set. These precise percentages can be found in Table 12. We can see that even if a term type is sometimes less represented in the training corpus, BERT (base and multi) often do a good job retrieving it. Figure 2 suggests that transfer learning for ATE is domain sensitive and that a careful choice of training data sets is undoubtedly needed for better performance since more training data does not always rhyme with better performances. Table 9: ATE scores (%) in terms of Precision (P), Recall (R) and F1-score (F1). For each validation corpus, the two remaining ones were used for fine-tuning. We contrast 3 usages of BERT : as a binary classifier, BERT-NER and BERT embeddings used with a biLSTM-CRF. We also contrast BERT's multi-lingual version as well as RoBERTa for English and Camembert for French (This table is similar to Table 2 of the main paper with extra models such distilbert and Roberta-large).

Figure 1 :

 1 Figure 1: Proportion (%) of each term type in the output list, the gold standard, and corpus used for training for the heart failure English test set. The proportion of out-of-domain terms in the training corpus is so small, it often looks like there are none. These precise percentages can be found in Table12.

Figure 2 :

 2 Figure 2: Illustration of BERT, BERT-multi and BERT (NER) models outputs, with regards to term types for the heart failure test sets and for all combinations of the training sets.

Table 1 :

 1 A Conditional Random Fields (CRF) layer is connected to the LSTM last output layer, enabling it to efficiently use neighboring tag information to better model the conditional probability of the output state sequence. Weights in vanilla LSTM can either be initialized with random values or with third-party word embeddings: here, BERT embeddings are used as input of our model to overcome the problem of low training data.

	Domain	# Tokens # Documents # Term lists
		en 468,711	44	1174
	corruption	fr 475,244	31	1217
		nl 470,242	49	1295
		en 102,654	89	1575
	dressage	fr 109,572	125	1183
		nl 103,851	125	1546
		en 314,618	38	1534
	wind energy fr 314,681	12	968
		nl 308,742	29	1245
		en 45,788	190	2585
	heart failure fr 46,751	215	2423
		nl 47,888	175	2257

Number of unique tokens and documents per corpus for English (en), French (fr) and Dutch (nl) as well as the size of the evaluation term lists.

Table 3 :

 3

Domain transfer results (F1%) of ATE on the English and French data sets (for the sake of clarity, Dutch results are put in the supplementary material).

Table 6 :

 6 Percentage of each term type in the true positives for BERT output list on the English heart failure test set with respect to transfer learning on different domains. The information about the other models can be found in the supplementary material.

	Table 6 represents the proportion of heart failure term
	types for BERT for different domain transfer config-
	urations. Regardless of the training sets, we see that
	specific and common terms are the most captured ele-
	ments 9 . However, the overall scores and the high per-
	centage of false positives indicate that there is still a big
	room for improvement. It should also be remarked that
	the named entities or out-of-domain terms categories
	are less represented in the training sets; making them
	harder to spot upon testing. The models' training was

  10 See Table5in the supplementary material for details.

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	tion Processing Systems 30, pages 5998-6008. Cur-
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	Zhang, X. and Fang, A. C. (2010). An ate system based
	on probabilistic relations between terms and syntactic
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	(JADT).

  Infrastructure and Average Runtime Our models are trained using a GeForce RTX 2080 GPU, for about 6 minutes per run for BERT, 4 minutes for BERT (NER) and 20 minutes for the BiLSTM-CRF.

	Domain	# ST # CT # NE # OOD Total
		en 278 644 248	6	1174
	corruption	fr 300 678 236	5	1217
		nl 310 731 249	6	1295
		en 780 309 421	71	1575
	dressage	fr 705 238 221	26	1183
		nl 1026 333 153	41	1546
		en 781 296 444	14	1534
	wind energy fr 444 308 195	21	968
		nl 577 342 305	21	1245
		en 1885 320 228	158 2585
	heart failure fr 1714 505 147	59	2423
		nl 1561 450 182	66	2257

11 https://github.com/ThilinaRajapakse/ simpletransformers 9.1.3.

Table 8 :

 8 Number of unique terms per corpus in three languages. Numbers are given for each type of terms: ST for Specific Terms, CT for Common Terms, NE for Named Entities, and OOD for Out of Domain terms. Total covers all the terms.

Data augmentation-based SMT, and NMT systems often require additional annotated bi-texts for training or improving language models. In clinical concept detection and bilingual term extraction, embedding models are used as features for deep neural approaches, such as biLSTM, but these methods also require enough annotated data to be trained on.

While various binary classifiers have been applied to ATE (see the Related Work section), to our knowledge, the only work that addressed ATE using BERT comes from the Ter-mEval shared task[START_REF] Rigouts Terryn | Termeval 2020: Shared task on automatic term extraction using the annotated corpora for term extraction research (acter) dataset[END_REF]

 3 The n-gram size is set empirically

.4 Within the IOB tags (PER, ORG, MISC, and LOC), no significant difference in performance has been observed.

Additional models such as bert-large-cased and distilroberta-base are presented in the supplementary material.

More than 25 features were tested. We retained the bestperforming ones: Frequency, TF-IDF, C-value, Termhood, and Specificity.

It should be noted that this work focuses on BERT-based strategies, and these baselines were implemented solely for comparison's sake.

Not all the teams submitted outputs for all languages.

See Table5in the supplementary material for details.


Coraline Chartier

Sytze Buwalda

Hélène Van

Den Berghe

Benjamin Nottelet

Tatiana Budtova
email: tatianabudtova@minesparis.psl.fr

Tuning the properties of porous chitosan: aerogels and cryogels

Keywords: density, specific surface area, porosity, freeze-drying, supercritical drying

Highlights

-Chitosan aerogels and cryogels were prepared via supercritical CO2 drying and freezedrying, respectively -The influence of process parameters on materials' structure and properties was studied -Aerogels possessed a higher density and higher specific surface area than cryogels -The absorption of simulated wound exudate was investigated

INTRODUCTION

A porous 3D biomaterial can be generated by removing the solvent from a "wet" polymer network. The properties of the porous material depend greatly on the drying method of the precursor [START_REF] Groult | Pectin hydrogels, aerogels, cryogels and xerogels: Influence of drying on structural and release properties[END_REF][START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF]. Aerogels are obtained via drying under supercritical (sc) conditions, most often using sc CO2. Since no liquid-vapor meniscus exists under sc conditions, capillary stresses, which usually lead to collapse of pore walls under ambient pressure drying, are no longer acting. The structure of the gel is therefore theoretically kept intact, leading to a nanostructured material with high porosity and high internal pores' surface area. This is in contrast with freeze-drying (for simplicity we will call such materials cryogels), where the growth of ice crystals may distort the network structure and lead to the formation of larger pores and a lower internal surface area.

Materials obtained via ambient pressure or vacuum drying, so-called xerogels, often have a high density and low porosity due to collapse of pore walls. Both aerogels and cryogels possess particular properties in terms of density, morphology, pore size distribution and surface area. The understanding of processing-structure-properties correlations is therefore essential for matching the requested application.

Chitosan is a semi-natural polymer which can be obtained via deacetylation of chitin which in turn is extracted from crustacean shells. It is a cationic polymer composed of D-glucosamine and N-acetyl-D-glucosamine linked by β (1→4) glycosidic bonds. Thanks to its antiviral, antibacterial and antifungal properties as well as its biocompatibility, chitosan has been frequently used for biomedical applications, including wound dressings and drug delivery systems [START_REF] Anitha | Chitin and chitosan in selected biomedical applications[END_REF][START_REF] Dash | Chitosan-A versatile semi-synthetic polymer in biomedical applications[END_REF][START_REF] Jiang | Chitosan as a biomaterial: structure, properties, and applications in tissue engineering and drug delivery[END_REF]. Several examples of porous chitosan-based biomaterials, including cryogels and aerogels, exist in literature, as reviewed recently [START_REF] Wei | Synthesis of chitosan aerogels as promising carriers for drug delivery: A review[END_REF][START_REF] Dragan | Advances in porous chitosan-based composite hydrogels: Synthesis and applications[END_REF]. For example, pH-responsive chitosan [START_REF] Radwan-Pragłowska | Biodegradable, pHresponsive chitosan aerogels for biomedical applications[END_REF] and hybrid chitosancellulose-graphene oxide [START_REF] Wang | pH-Controlled drug delivery with hybrid aerogel of chitosan, carboxymethyl cellulose and graphene oxide as the carrier[END_REF] cryogels were developed for drug delivery applications. Drug-loaded chitosan aerogel particles prepared via the dropping [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF] or jet cutting method [START_REF] López-Iglesias | Jet Cutting Technique for the Production of Chitosan Aerogel Microparticles Loaded with Vancomycin[END_REF] were suggested for wound healing applications.

In order to obtain chitosan aerogels or cryogels, various approaches exist. In all cases chitosan is first dissolved, usually in acidic medium. In order to form a network, chitosan can then be either crosslinked via its amino groups (using formaldehyde [START_REF] Takeshita | Chitosan Aerogels: Transparent, Flexible Thermal Insulators[END_REF], urea [START_REF] Takeshita | Transparent, Aldehyde-Free Chitosan Aerogel[END_REF], glutaraldehyde [START_REF] Takeshita | Chemistry of Chitosan Aerogels: Three-Dimensional Pore Control for Tailored Applications[END_REF] or sodium tripolyphosphate [START_REF] Obaidat | Drying Using Supercritical Fluid Technology as a Potential Method for Preparation of Chitosan Aerogel Microparticles[END_REF]), or directly coagulated in a non-solvent (for example, in a basic solution). In the latter case non-solvent induced phase separation (or immersion precipitation) occurs: chitosan forms a network with non-solvent in the pores. To use drying with sc CO2, the liquid in the pores of a network (crosslinked or not) must be miscible with CO2, and thus solvent exchange step is often applied. Quignard et al. used the non-solvent induced phase separation method for the preparation of chitosan beads in NaOH-water; NaOH was then removed by washing the beads in water followed by exchange of water to ethanol and sc CO2 drying, resulting in chitosan aerogel beads [START_REF] Quignard | Aerogel materials from marine polysaccharides[END_REF]. The same approach was used in refs. [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF] and [START_REF] López-Iglesias | Jet Cutting Technique for the Production of Chitosan Aerogel Microparticles Loaded with Vancomycin[END_REF]. Chemical crosslinking is supposed to lead to better mechanical properties and a more homogeneous network structure [START_REF] Takeshita | Chemistry of Chitosan Aerogels: Three-Dimensional Pore Control for Tailored Applications[END_REF], but the use of toxic crosslinkers (e.g. glutaraldehyde) may lead to adverse effects in biomedical applications.

In the vast majority of publications only one method of drying is used for the preparation of the chitosan porous material, which impedes the in-depth understanding of processing-structureproperties correlations of chitosan porous materials. To the best of our knowledge, only two articles compare the properties of sc dried and freeze-dried chitosan starting from the same precursor, and in both cases chitosan was crosslinked. Obaidat et al. prepared sc CO2 and freeze-dried beads based on chitosan crosslinked with sodium tripolyphosphate [START_REF] Obaidat | Drying Using Supercritical Fluid Technology as a Potential Method for Preparation of Chitosan Aerogel Microparticles[END_REF]. The specific surface area of aerogels was 70 -100 m 2 /g and for cryogels it was not detectable. Takeshita et al. made sc CO2 and freezedried (from water and from tert-butanol) chitosan crosslinked with formaldehyde and reported much a higher value for the specific surface area of aerogels, almost 500 m 2 /g. For the cryogel, as in the case above, the surface area was not detectable [START_REF] Takeshita | Formation of Nanofibrous Structure in Biopolymer Aerogel during Supercritical CO 2 Processing: The Case of Chitosan Aerogel[END_REF]. In the present work, for the first time, the influence of drying conditions (freeze-drying versus sc drying) on the structural and physicochemical properties of non-crosslinked chitosan porous materials was investigated starting from the same chitosan precursor. We also analyzed the influence of chitosan and NaOH concentrations in the coagulation bath and of aging time. In the view of potential biomedical applications of these porous chitosan materials, the absorption of simulated wound exudate was evaluated.

EXPERIMENTAL

Materials

All chemicals were used as received. Chitosan of high molecular weight (600 -800 kDa according to the manufacturer) was purchased from Acros; the exact molecular weight was determined with viscometry and is presented in the Results and Discussion section. The degree of acetylation was determined by infrared spectroscopy with the method described by Moore and

Roberts [START_REF] Moore | Determination of the degree of N-acetylation of chitosan[END_REF] (see Methods section). Glacial acetic acid, ethanol (purity higher than 99 %) and sodium hydroxide pellets (analytical reagent grade) were supplied by Fisher Chemical. Water was distilled.

Methods

Preparation of chitosan cryogels and aerogels

Chitosan was dissolved in a solution of 2 % v/v acetic acid under mechanical stirring at room temperature for several hours. The concentration of chitosan was varied from 1 to 10 % wt/v. Around 6 mL of chitosan solution was poured into polypropylene molds of 27.5 mm in diameter with holes in the walls of 1 mm in diameter. The molds were immersed in 100 mL of aqueous NaOH solution (0.1 -4 M at various durations called "aging") for non-solvent induced phase separation. As NaOH-water is chitosan non-solvent, the polymer coagulated, but most of the samples kept their 3D shape. The holes in the mold were made for non-solvent penetration from all sides of chitosan solution thus leading to homogeneous coagulation. NaOH was then washed out with water by placing the sample in consecutive water baths, resulting in a "hydrogel" (Fig. 1).

Figure 1

In order to obtain "aerogels", drying was performed with sc CO2 (Fig. 1). As water is not miscible with CO2, water in the hydrogels was replaced by ethanol as follows. The chitosan hydrogels were gently demolded and immersed in mixtures of ethanol and distilled water. The ethanol fraction was gradually increased over the course of three days (10/90, 25/75, 50/50, 75/25, 90/10, 100/0) to remove water. To ensure a complete solvent exchange, several washing steps with pure ethanol were performed during two days. This solvent exchange resulted in chitosan "alcogels" (Fig. 1).

Drying with sc CO2 was performed as described elsewhere [START_REF] Groult | Thermal conductivity/structure correlations in thermal super-insulating pectin aerogels[END_REF][START_REF] Druel | Starch Aerogels: A Member of the Family of Thermal Superinsulating Materials[END_REF]. The system was pressurized at 50 bar and 37 °C, and ethanol was slowly washed with gaseous CO2. Afterwards, the pressure in the autoclave was increased to 80 bars to be above the CO2 critical point. The sc CO2 solubilized the residual non-solvent inside the sample pores. At 37°C, a dynamic washing step was performed with an output of 5 kg of CO2/h for 1 h. It was followed by a static mode of 1 -2 h in the same conditions and then a dynamic step for 2 h again. The system was then depressurized at 4 bar/h and cooled to room temperature before opening the autoclave.

To obtain "cryogels", hydrogels were freeze-dried by first immersing in liquid nitrogen (-196 °C) for 5 min and then placing in a freeze-dryer (Cryotec Cosmos 80) for lyophilization for 48 h.

"Xerogel" was obtained from a hydrogel made from 5 % wt/v chitosan solution coagulated in 4 M NaOH, aged for 24 h and dried at 55°C under vacuum until the constant weight.

Characterization

Chitosan molecular weight was determined from the intrinsic viscosity using the Mark-Houwink equation using an aqueous solution containing 0.1 M of acetic acid and 0.2 M of NaCl at 25 °C [START_REF] Roberts | Determination of the viscometric constants for chitosan[END_REF]:

[𝜂] = 𝐾𝑀 𝛼 (1)
with K = 1.81 x 10 5 dL/g and  = 0.93 [START_REF] Roberts | Determination of the viscometric constants for chitosan[END_REF].

Chitosan degree of acetylation (DA) was determined by infrared spectroscopy with the method described by Moore and Roberts [START_REF] Moore | Determination of the degree of N-acetylation of chitosan[END_REF]. The chitosan powder (4 mg) and 100 mg of KBr were dried for 4 h at 80 °C and mechanically blended to make a disk. Then the degree of DA was determined with the equation:

𝐷𝐴(%) = (𝐴 1655 𝐴 3450 ⁄ ) × (100 1.33 ⁄ ) (2)
with A1655 the absorption at 1655 cm -1 of the amide I band and A3450 the absorption at 3450 cm -1 of the hydroxyl groups.

The evolution of sample volume at each processing step (for aerogels: from solution to alcogel and from alcogel to aerogel; for cryogels: from solution to hydrogel and from hydrogel to cryogel) and total shrinkage (from solution to dry matter, see Fig. 1) was monitored. As samples were of cylindrical shape, their diameter and height were measured, and volume shrinkage was calculated according to the following equation:

𝑆ℎ𝑟𝑖𝑛𝑘𝑎𝑔𝑒 (%) = (𝑉 𝑖 -𝑉 𝑓 ) × 100 𝑉 𝑖 ⁄ (4)
where Vi and Vf are sample volumes before and after the step, respectively.

The bulk density bulk was measured with an Envelope Density Analyzer Geopyc 1360 (Micromeritics) with a calibrated chamber using DryFlo fine powder. Porosity was calculated from bulk and skeletal density (skeletal, 1.446 ± 0.116 g/cm 3 for chitosan [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF]) with equation [START_REF] Jiang | Chitosan as a biomaterial: structure, properties, and applications in tissue engineering and drug delivery[END_REF].

𝑃𝑜𝑟𝑜𝑠𝑖𝑡𝑦 (%) = (𝜌 𝑠𝑘𝑒𝑙𝑒𝑡𝑎𝑙 -𝜌 𝑏𝑢𝑙𝑘 ) × 100 𝜌 𝑠𝑘𝑒𝑙𝑒𝑡𝑎𝑙 ⁄ (5)
Pore specific volume was estimated using bulk and skeletal densities:

𝑉 𝑝𝑜𝑟𝑒 = (1 𝜌 𝑏𝑢𝑙𝑘 ⁄ ) -(1 𝜌 𝑆𝑘𝑒𝑙𝑒𝑡𝑎𝑙 ⁄ ) (6)
The specific surface area was measured using a Micromeritics ASAP 2020 employing the Brunauer, Emmett et Teller (BET) method. Prior to measurement the samples were degassed in a high vacuum at 70 °C for 10 h.

The morphology of the samples was studied with a Scanning Electron Microscope MAIA (Tescan) equipped with a field emission gun at an accelerating voltage of 3 keV. A layer of platinum was coated (14 nm) using a Q150T Quarum rotating metallizer.

It is now accepted that it is not possible to obtain the complete pore size distribution in bioaerogels using nitrogen adsorption (BJH method) or mercury porosimetry [START_REF] Groult | Thermal conductivity/structure correlations in thermal super-insulating pectin aerogels[END_REF][START_REF] Tabernero | Role of rheological properties on physical chitosan aerogels obtained by supercritical drying[END_REF][START_REF] Robitzer | Natural materials with high surface area. Physisorption methods for the characterization of the texture and surface of polysaccharide aerogels[END_REF]. The BJH approach takes into account mesopores and small macropores thus providing only 10-15 % of the total pore volume, and mercury does not enter the pores but compresses the sample. The pore size distribution in chitosan aerogels and cryogels was therefore roughly estimated with the software Visilog from the SEM images; at least 270 pores per formulation were analyzed.

X-ray diffraction patterns were obtained on powder samples with a 'XPERT-PRO' diffractometer from PANalytical equipped with a θ/θ 3050/60 goniometer, a CuKα source operated at 40 kV and 30 mA, and the measurement program 'ScanPixcel'. The diffraction angle 2θ was from 5° to 40° with a 0.08° step.

Dried samples were immersed in simulated wound exudate (SWE) composed of 8.3 g/L of NaCl and 0.37 g/L of CaCl2 at 31 °C (no viscosity modifier was used in agreement with the norm EN 13726-1). The evolution of sample weight Wt in time was measured during 48 h, and SWE absorption was calculated with the following equation:

𝐴𝑏𝑠𝑜𝑟𝑝𝑡𝑖𝑜𝑛(%) = ((𝑊 𝑡 -𝑊 𝑖 ) × 100) 𝑊 𝑖 ⁄ ( 7
)
where Wi is sample initial weight.

3.

RESULTS AND DISCUSSION

The results are presented as follows. First, the determination of the molecular weight and degree of acetylation of chitosan will be shown. Then, each material property, namely volume shrinkage, density, porosity, morphology, specific surface area and absorption of SWE will be presented and discussed for aerogels and cryogels as a function of various parameters such as the chitosan concentration, the concentration of NaOH in the coagulation bath, the aging time and the method of drying.

Determination of chitosan molecular weight and degree of acetylation

The intrinsic viscosity of chitosan was determined as described in Methods [START_REF] Roberts | Determination of the viscometric constants for chitosan[END_REF] and found to be 4.87 dL/g. The molecular weight was estimated to be around 691000 g/mol, which is in the range of the molecular weight given by the manufacturer, 600 -800 kg/mol.

The degree of acetylation was determined as described in the Methods section [START_REF] Moore | Determination of the degree of N-acetylation of chitosan[END_REF] and found to be 10 ± 2 %.

Visual appearance

Photos of chitosan aerogels and cryogels made under different conditions are presented in Fig. 2. Higher is the concentration of chitosan, more stable is the shape of the hydrogel (and of the corresponding aerogel or cryogel), as seen in Fig. 2. For example, for 1 % wt/v of dissolved chitosan coagulated in 4 M NaOH, a fluffy aerogel with no distinct shape was obtained, and no adequate characterization of this sample was possible. A minimum of 3 % wt/v chitosan is necessary for the sample to keep the shape of the mold upon coagulation; a similar result was observed for cellulose aerogels despite that the molecular weight of cellulose was much lower, around 30-35 kDa [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF]. No influence on the aerogel shape of NaOH concentration in the coagulation bath above 1 M was observed. Below 1 M NaOH, the hydrogel was soft and weak and the corresponding aerogel as well.

Figure 2 The cryogels have a similar visual appearance as aerogels (Fig. 2), but cracks are visible along the samples. They were formed during freezing because of the growing ice crystals. At higher chitosan concentrations, the samples present less cracks and are harder.

Xerogel photo is shown in Fig. S1 of the Supporting Information. The xerogel is brownish and partly translucent, with distorted shape and cracks most probably because of the significant and heterogeneous shrinkage.

Shrinkage

Volume shrinkage at different steps of the process and total shrinkage (eq. 2) are presented in Fig. 3 for aerogels and cryogels as a function of the chitosan concentration in the initial solution (Fig. 3a), of the NaOH concentration in the coagulation bath (Fig. 3b) and of the aging time (Fig. 3c). Because of a significant total shrinkage of the xerogel, around 80 %, only one sample was prepared and analyzed. Data known from literature for non-crosslinked chitosan aerogels and cryogels are also shown in Table S1 for comparison.

During coagulation and solvent exchange, a small volume decrease within 20 % was observed (Fig. 3). Polymer chains are supposed to strongly contract when a solvent is replaced by a nonsolvent; this is well known for synthetic polyelectrolyte polymer gels [START_REF] Tanaka | Collapse of Gels and the Critical Endpoint[END_REF]. Interestingly, chitosan is not shrinking much, most probably because the non-solvent used is an aqueous medium which has a high affinity for chitosan, and also because of polysaccharide chain rigidity. Rather low shrinkage in a non-solvent was also observed for cellulose, pectin and high-amylose starch [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF][START_REF] Druel | Starch Aerogels: A Member of the Family of Thermal Superinsulating Materials[END_REF][START_REF] Groult | Tuning structure and properties of pectin aerogels[END_REF]. Shrinkage during sc CO2 drying is similar to the one during coagulation and solvent exchange, leading to a total shrinkage around 30 -40 % which does not depend on process parameters within the intervals studied and experimental errors. Neither polymer concentration (Fig. 3a), which usually strengthens the network to better resist the shrinkage [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF][START_REF] Groult | Tuning structure and properties of pectin aerogels[END_REF], nor NaOH concentration (above 0.5 M NaOH) (Fig. 3b) or aging time (Fig. 3c) influence shrinkage, within experimental errors. At 0.1 M NaOH, despite that the amount of NaOH is superior to chitosan cationic groups, shrinkage is heterogeneous not allowing adequate determination of sample volume, see the photo and SEM image in Fig. S2. We suppose that at low NaOH concentrations the chitosan network was not well "stabilized" during coagulation and partially collapsed. We hypothesize that the time needed for complete gel coagulation, which is governed by diffusion and polymer/non-solvent interactions, is an important parameter influencing shrinkage and aerogel properties. Thus the 0.1 M NaOH concentration will not be used in the following. It should be noted that our data follow the trend of shrinkage vs aging time reported in [START_REF] López-Iglesias | Jet Cutting Technique for the Production of Chitosan Aerogel Microparticles Loaded with Vancomycin[END_REF]: higher the aging time, lower the shrinkage with no further dependence when aging is longer than 24 h. In the following, aging time is kept at 24 h. Overall, the shrinkage obtained is moderate compared to that reported for some chitosan aerogels (Table S1) which can be up to 60 % [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF][START_REF] Quignard | Aerogel materials from marine polysaccharides[END_REF] and even 90 % [START_REF] Takeshita | Formation of Nanofibrous Structure in Biopolymer Aerogel during Supercritical CO 2 Processing: The Case of Chitosan Aerogel[END_REF], or for some other bio-aerogels, between 50 -95 % [START_REF] Quignard | Aerogel materials from marine polysaccharides[END_REF]. However, because of the numerous parameters involved in the preparation of bio-aerogels (polymer molecular weight, charge and chain flexibility, solvent pH, ionic strength, way of structure formation (via non-solvent induced phase separation or gelation induced by crosslinking), type of non-solvent, way of coagulation, etc.), there is still no clear understanding and control of volume shrinkage, and as a consequence, of aerogel bulk density (see next section).

Figure 3 Cryogels showed no volume shrinkage during freezing and drying, within the experimental errors (Fig. 3a). Lower shrinkage for cryogels as compared to that for aerogels has been reported

for cellulose and starch [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF][START_REF] Zou | Tailoring the morphology and properties of starch aerogels and cryogels via starch source and process parameter[END_REF]. This is an expected result as in the preparation of cryogels there is no step of hydrogel immersion in ethanol and in CO2, both being non-solvents of native polysaccharides.

Density, porosity, specific surface area and structure

Bulk density and porosity of aerogels and cryogels are shown in Fig. 4a, b and c as a function of chitosan concentration in the initial solution, NaOH concentration in the coagulation bath and aging time, respectively. The theoretical density, calculated for a hypothetical case of zero shrinkage, is also shown for comparison. Bulk density of aerogels was between 0.07 and 0.26 g/cm 3 , similar to the values reported in literature (Table S1) [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF][START_REF] López-Iglesias | Jet Cutting Technique for the Production of Chitosan Aerogel Microparticles Loaded with Vancomycin[END_REF][START_REF] Quignard | Aerogel materials from marine polysaccharides[END_REF][START_REF] Tabernero | Role of rheological properties on physical chitosan aerogels obtained by supercritical drying[END_REF][START_REF] Di Renzo | Hierarchical Macroporosity Induced by Constrained Syneresis in Core-Shell Polysaccharide Composites[END_REF][START_REF] Díez-Municio | Supercritical CO2 impregnation of lactulose on chitosan: A comparison between scaffolds and microspheres form[END_REF][START_REF] Lovskaya | Chitosan-Based Aerogel Particles as Highly Effective Local Hemostatic Agents. Production Process and In Vivo Evaluations[END_REF]. The density of aerogels is higher than the theoretical density due to the volume shrinkage during solvent exchange and drying. The bulk density of cryogels is lower than that of aerogels when made from the same chitosan concentration in solution (Fig. 4), it is between 0.03 and 0.12 g/cm 3 and is almost equal to the theoretical density thanks to the negligible volume shrinkage (Fig. 3a). As expected, higher is the chitosan concentration, higher is aerogel and cryogel density and lower is the porosity and pore volume (Fig. S4). The density of chitosan xerogel is around 1.30 ±0.15 g/cm 3 and thus porosity is very low, around 10 % (Fig. 4a).

Figure 4

The sodium hydroxide concentration does not influence density and porosity of aerogels above 0.5 M NaOH (Fig. 4b). At 4 M NaOH aging time also does not influence the density and the porosity (Fig. 4c), confirming the observations reported in the literature [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF].

The specific surface area of chitosan aerogels varies within 200 -270 m 2 /g (Fig. 5); it slightly increases with the chitosan concentration (Fig. 5a). The values obtained are similar to those reported in literature (Table S1) [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF][START_REF] López-Iglesias | Jet Cutting Technique for the Production of Chitosan Aerogel Microparticles Loaded with Vancomycin[END_REF][START_REF] Quignard | Aerogel materials from marine polysaccharides[END_REF][START_REF] Tabernero | Role of rheological properties on physical chitosan aerogels obtained by supercritical drying[END_REF][START_REF] Di Renzo | Hierarchical Macroporosity Induced by Constrained Syneresis in Core-Shell Polysaccharide Composites[END_REF][START_REF] Díez-Municio | Supercritical CO2 impregnation of lactulose on chitosan: A comparison between scaffolds and microspheres form[END_REF][START_REF] Lovskaya | Chitosan-Based Aerogel Particles as Highly Effective Local Hemostatic Agents. Production Process and In Vivo Evaluations[END_REF]]. An increase in NaOH concentration in the coagulation bath slightly increases specific surface area (Fig. 5b).

Figure 5

The surface area of cryogels is much lower, around 50 -70 m 2 /g (Fig. 5a). The reason is that pores in cryogels are larger as they constitute the space left by sublimated ice crystals.

Nevertheless, the specific surface area of as-prepared chitosan cryogels is higher than that reported in literature (few m²/g) [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF][START_REF] Díez-Municio | Supercritical CO2 impregnation of lactulose on chitosan: A comparison between scaffolds and microspheres form[END_REF][START_REF] Wang | Fabrication of a novel porous PGAchitosan hybrid matrix for tissue engineering[END_REF][START_REF] Sun | Porous Nanocomposite Comprising Ultralong Hydroxyapatite Nanowires Decorated with Zinc-Containing Nanoparticles and Chitosan: Synthesis and Application in Bone Defect Repair[END_REF][START_REF] Tan | Fabrication and evaluation of porous keratin/chitosan (KCS) scaffolds for effectively accelerating wound healing[END_REF], and of cellulose II and starch cryogels, being within 10 -60 m 2 /g and below 20 m 2 /g, respectively [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF][START_REF] Zou | Tailoring the morphology and properties of starch aerogels and cryogels via starch source and process parameter[END_REF].

The inner morphology of chitosan aerogels and cryogels was studied by SEM, representative examples for each type of material made from chitosan solutions of the same concentration are shown in Fig. 6 at different magnifications. The morphology depends on the drying technique: larger pores were obtained with freeze-drying confirming the lower specific surface area of cryogels as compared to that of aerogels. Similar results have been reported for cellulose II and starch-based aerogels and cryogels [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF][START_REF] Zou | Tailoring the morphology and properties of starch aerogels and cryogels via starch source and process parameter[END_REF][START_REF] Buchtová | Mechanical properties of cellulose aerogels and cryogels[END_REF]. A SEM image of the xerogel morphology (Figure S1) shows the absence of pores, as expected from its very low porosity.

We roughly estimated the pore size of aerogels and cryogels from SEM images; the results are shown in Fig. S3 of the Supporting Information. In both aerogels and cryogels, the pore size is slightly decreasing with the increase of polymer concentration; the same trend was reported for cellulose aerogels and cryogels [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF]. Interestingly, the sizes of the pores in cryogels are rather low, from several hundreds of nanometers to few microns which is much smaller compared to other non-crosslinked chitosan-based materials which underwent freeze-drying (pores of several tens up to few hundreds of microns [START_REF] Fan | Preparation and characterization of antibacterial polyvinyl alcohol/chitosan sponge and potential applied for wound dressing[END_REF]). The smaller pore size of the chitosan cryogels in the current study compared to many other chitosan freeze-dried materials can be explained as follows. The size of the pores in a freeze-dried material strongly depends on the state of the matter before drying.

For example, a polysaccharide solution (agarose [START_REF] Lozinsky | Cryostructuring of polymer systems. XXIX. Preparation and characterization of supermacroporous (spongy) agarose-based cryogels used as three-dimensional scaffolds for culturing insulin-producing cell aggregates[END_REF], hyaluronic acid [START_REF] Cai | Freeze-Thaw-Induced Gelation of Hyaluronan: Physical Cryostructuration Correlated with Intermolecular Associations and Molecular Conformation[END_REF]) can undergo cryogelation during freezing resulting in very large macropores of several tens of microns. Pores and channels from few to several tens of microns were reported for a freeze-dried nanocellulose suspension [START_REF] Martoïa | Cellulose nanofibril foams: Links between ice-templating conditions, microstructures and mechanical properties[END_REF]. Freeze-dried ultrasonicated chitosan-alginate, chitosan-collagen and chitosangelatin blends also possess pores of several tens of microns [START_REF] Naghshineh | Preparation of Chitosan, Sodium Alginate, Gelatin and Collagen Biodegradable Sponge Composites and their Application in Wound Healing and Curcumin Delivery[END_REF]. The evolution of the morphology of a chitosan network (chitosan solution was dropped in tripolyphosphate solution for different duration, beads post-crosslinked with ethylene glycol diglycidyl ether and freeze-dried) at different stages of coagulation was demonstrated in ref. [START_REF] Mi | Adsorption of indomethacin onto chemically modified chitosan beads[END_REF]. Ionic bonds are formed between phosphate groups of tripolyphosphate and amine groups of chitosan. At the early stage of coagulation (called phase inversion in ref. [START_REF] Mi | Adsorption of indomethacin onto chemically modified chitosan beads[END_REF]), when the network is not yet formed, large pores and channels of several hundreds of microns were obtained; with the progress of coagulation pores became smaller and channels disappeared. All examples given above demonstrate that in weak gels the morphology of freeze-dried matter is adapting to the shape of growing ice crystals. In our case, the network morphology in the hydrogel is fixed before drying as coagulation is completed, and growing ice crystals are deforming and enlarging the pores but not destroying them. Similar results were obtained for cellulose II and starch hydrogels: the network is formed either during coagulation for cellulose [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF], or during retrogradation for starch [START_REF] Zou | Tailoring the morphology and properties of starch aerogels and cryogels via starch source and process parameter[END_REF].

Buchtová et al. [START_REF] Buchtová | Cellulose aero-, cryo-and xerogels: towards understanding of morphology control[END_REF] suggested that an increase of the concentration in a cellulose aerogel decreases the pore size thus increasing the specific surface area supposing that pore wall thickness remains the same. A very minor decrease in the pore size of aerogels can be seen (Fig. S3 of the Supporting Information), which corroborates the slight increase in specific surface area observed in Fig. 5a, but no particular trend can be deduced considering a large dispersion of data and the roughness of the approach.

The average size of pore walls in aerogels and cryogels was also estimated from SEM images: the values are 14.4 ±3.9 nm and 32 ±14 nm, respectively, which results in approximately 1 nm and 18 nm after subtracting the thickness (14 nm) of the layer of sputtered platinum. These results were compared with the calculated pore wall thickness D assuming the pore walls are ideal rods of uniform thickness and having the same skeletal density as chitosan:

𝐷 = 4 (𝜌 𝑏𝑢𝑙𝑘 × 𝑆 𝐴 ) ⁄ (7)
The pore wall thickness for aerogels and cryogels was calculated to be around 1.3 nm and 13 nm, respectively, which fits well with the experimental observation.

The XRD pattern of the initial chitosan powder and of grinded aerogel and cryogel are presented in Fig. 7. The initial powder shows a very broad peak around 10° and a sharper peak centered around 20°, similar to the ones reported for chitins and chitosan of various degrees of acetylation and typical for a semi-crystalline chitosan [START_REF] Cho | Preparation and Solubility in Acid and Water of Partially Deacetylated Chitins[END_REF][START_REF] Barreiro-Iglesias | Preparation of chitosan beads by simultaneous cross-linking/insolubilisation in basic pH[END_REF][START_REF] Prashanth | Solid state structure of chitosan prepared under different Ndeacetylating conditions[END_REF][START_REF] Yen | Physicochemical characterization of chitin and chitosan from crab shells[END_REF]. Aerogels and cryogels show peaks at the same angles but the one at 10° is sharper than that of chitosan powder. This sharp peak is characteristic of a hydrated crystalline chitosan [START_REF] Barreiro-Iglesias | Preparation of chitosan beads by simultaneous cross-linking/insolubilisation in basic pH[END_REF]. As cryogels and aerogels present a higher specific surface area than that of the powder, we suppose that these porous materials may adsorb more water vapours than chitosan powder explaining the sharper peak. Aging time and sodium hydroxide concentration do not influence the XRD pattern (data not shown) within the experimental errors.

Absorption of simulated wound exudate

In view of the potential application of chitosan aerogels and cryogels as wound dressings, the absorption of simulated wound exudate (SWE) was studied as a function of the chitosan concentration. Moisture balance is important to guarantee a good healing process and limit infections [START_REF] Turner | Hospital usage of absorbent dressings[END_REF]. The absorption was monitored during 48 h, which is the duration a dressing is typically used before being replaced [START_REF] Lindholm | Diagnosis and treatment impacts on wound care efficiency drivers: real-world analysis[END_REF]. Fig. 8a shows that higher is the concentration of chitosan in the initial solution, and thus lower the porosity (Fig. 4) and pore volume (Fig. S3), lower is the absorption of SWE by both aerogels and cryogels. In all cases, a plateau value is reached within one hour. The absorption values obtained correspond to the higher values of chitosan cryogels and aerogels reported in the literature [START_REF] López-Iglesias | Vancomycin-loaded chitosan aerogel particles for chronic wound applications[END_REF][START_REF] López-Iglesias | Jet Cutting Technique for the Production of Chitosan Aerogel Microparticles Loaded with Vancomycin[END_REF][START_REF] Lovskaya | Chitosan-Based Aerogel Particles as Highly Effective Local Hemostatic Agents. Production Process and In Vivo Evaluations[END_REF][START_REF] Wang | Fabrication of a novel porous PGAchitosan hybrid matrix for tissue engineering[END_REF][START_REF] Sun | Porous Nanocomposite Comprising Ultralong Hydroxyapatite Nanowires Decorated with Zinc-Containing Nanoparticles and Chitosan: Synthesis and Application in Bone Defect Repair[END_REF][START_REF] Tan | Fabrication and evaluation of porous keratin/chitosan (KCS) scaffolds for effectively accelerating wound healing[END_REF][START_REF] Fan | Preparation and characterization of antibacterial polyvinyl alcohol/chitosan sponge and potential applied for wound dressing[END_REF] (Table S1). Because of their high absorption capacity, these materials seem suitable for healing of moist wounds where fluid absorption is necessary to limit bacterial proliferation. In Fig. 8b, the amount of fluid absorbed after one hour shows that higher is the porosity, higher is the absorption, as expected. Cryogels and aerogels of similar porosity show similar values of absorption. Chitosan concentration increases density and decreases porosity of both types of materials, as expected.

The main parameter that changes the properties is the method of drying. Supercritical drying allows the preservation of the morphology of the hydrogels resulting in a specific surface area of 200-270 m²/g. Due to the moderate shrinkage of the samples during solvent exchange and drying (within 30 %), the bulk density of aerogels is low, between 0.07 and 0.26 g/cm 3 . Freeze-drying distorted the morphology of hydrogels due to the growth of ice crystals resulting in larger pores compared to aerogels and in a lower specific surface area. Furthermore, cryogels were of lower density than aerogels and thus of higher porosity. Nevertheless, the pore sizes in cryogels were surprisingly small (from a few hundreds of nanometers to a few microns) as compared to chitosan materials reported in literature which underwent freeze-drying. It indicates a strong capacity of the network to resist to deformations induced by growing ice crystals during freezing.

In view of the high and tunable SWE absorption, the well-known biocompatibility of chitosan and the small pores which prevent cell ingrowth, these porous chitosan materials are promising materials for wound dressing applications.

Legends

Figure 8 4 .

 4 Figure 8

Figure 1 .

 1 Figure 1. Schematic representation of the preparation of chitosan aerogels and cryogels.

Figure 2 .

 2 Figure 2. Photos of chitosan aerogels and cryogels. Top line: aerogels made from solutions of

Figure 3 .

 3 Figure 3. Volume shrinkage during the preparation of chitosan aerogels, cryogels and xerogel as a

Figure 4 .

 4 Figure 4. Bulk density (in black) and porosity (in blue) of chitosan aerogels (filled circles), cryogels

Figure 5 .

 5 Figure 5. Specific surface area of aerogels (filled circles) and cryogels (filled triangles) as a

Figure 6 .

 6 Figure 6. SEM images of chitosan aerogels (top line) and cryogels (bottom line). The chitosan

Figure 7 .

 7 Figure 7. XRD diffraction patterns of the initial chitosan powder, cryogel and aerogel. The cryogel

Figure 8 .

 8 Figure 8. (a) Absorption kinetics of SWE at 30 °C for cryogels and aerogels made from 3, 4, 5, 6,

Figure 3 International 30 Figure 4 5 Figure 8

 330458 Figure 3

Figure S1

 S1 Figure S1

1 2 3 Figure S2 4 8 Figure S3 9

 13489 Figure S2 4 Photo and SEM image of chitosan aerogel sample made from 5% wt/v of chitosan solution, 5 coagulated in 0.1M of NaOH and aged for 24 h.67

Figure S4 4 7 8 [ 1 ] 11 [ 2 ]

 4781112 Figure S4 4Specific pore volume in chitosan aerogels (circles) and cryogels (triangles). Lines are given to 5 guide the eye. 6 7 References 8[1] C. López-Iglesias, J. Barros, I. Ardao, F.J. Monteiro, C. Alvarez-Lorenzo, J.L. Gómez-Amoza, C.A. García-

12 García-

 12 González, Jet Cutting Technique for the Production of Chitosan Aerogel Microparticles Loaded 13 with Vancomycin, Polymers. 12 (2020) 273. https://doi.org/10.3390/polym12020273.

14 [ 3 ]

 143 F. Quignard, R. Valentin, F. Di Renzo, Aerogel materials from marine polysaccharides, New Journal of

Table S1 :

 S1 Characteristics of aerogels and cryogels made from non-crosslinked chitosan as a function of processing parameters, taken from literature.Density values marked with "*" were calculated from the data reported for porosity, with ρs = 1.446 g/cm 3[START_REF] Groult | Pectin hydrogels, aerogels, cryogels and xerogels: Influence of drying on structural and release properties[END_REF]. FzD: freeze-drying, ScD: supercritical drying.

												740% of
	1	N.A.	125	0.1 M	12	4	ScD	N.A.	0.0569	96.77	262	water in 30	[6]
												min
												589% of
	Chitosan concentrat ion (%wt/v) 1 1	DA N.A. N.A.	Molecular weight (kDa) 294 343	Acetic acid concentrat ion 0.1 M 0.1 M	Aging time (h) 12 12	NaOH concentrat ion (M) 4 4	Drying method ScD ScD	Volume shrinkage (%) N.A. N.A.	0.0761 Bulk density (g/cm 3 ) 0.0802	95.89 Porosity (%) 96.16	254 Specific surface area (m²/g) 243	water in 30 min Absorption 483% of (%) water in 30	[6] Reference [6]
	2.5	10	200 -400	1%	0.5	0.1	ScD	55.8	0.046	96.8	360	N.A. min	[1]
	2.5 1 2	10 N.A. N.A.	200 -400 250 250	1% 3% 3%	1 N.A. N.A.	0.1 1 1	ScD ScD ScD	53.3 N.A. N.A.	0.046 0.090 0.112	96.8 94 92	115 343 125	380% of N.A. PBS in 24 h N.A.	[7] [1] [7]
	2.5 3	10 N.A.	200 -400 250	1% 3%	2 N.A.	0.1 1	ScD ScD	52.6 N.A.	0.049 0.137	96.6 90	386 110	N.A. N.A.	[1] [7]
	2.5 2.5 3	10 10 N.A.	200 -400 200 -400 N.A.	1% 1% 1%	4 10 12	0.1 0.1 1	ScD ScD FzD	41.3 38.3 N.A.	0.041 0.041 0.1414*	97.2 97.2 90.22	479 257 N.A.	N.A. N.A. 1365% of water in 24 h	[1] [1] [8]
	2.5 2.5 3	10 10 6	200 -400 200 -400 440	1% 1% 0.2M	24 N.A. 0.5	0.1 0.1 1	ScD FzD FzD	35.0 95.9 N.A.	0.042 0.535 0.3326	97.1 63.0 77	324 N.A. N.A.	N.A. N.A. 310% of PBS in 13 h	[1] [1] [9]
	2 N.A.	10 10	200 -400 100	1% N.A	1 N.A.	0.2 N.A.	ScD FzD	N.A. N.A.	N.A. 0.1446	N.A. 90	N.A. N.A.	900% of PBS in 24 h 250% of PBS in 2h	[2] [10]
	2.5 1 2	5 10 10	200 500 50	0.055 M 0.055 M 1%	N.A. N.A. N.A.	4 4 N.A.	ScD ScD FzD	60 N.A. N.A.	N.A. 0.0289* 0.2169*	N.A. 98 85	330 175 N.A.	N.A. N.A. 900% of water in 24 h	[3] [4] [11]
	1	15	350	2%	N.A.	1.25	ScD	N.A.	0.1605*	88.9	111	N.A.	[5]
	1	15	350	2%	N.A.	1.25	FzD	N.A.	0.0216*	98.3	3.6	N.A.	[5]
												963% of
	1	N.A.	111	0.1 M	12	4	ScD	N.A.	0.0488	97.94	301	water in 30	[6]
												min

International journal of biological macromolecules

Chemistry.32 (2008) 1300. https://doi.org/10.1039/b808218a.

polyvinyl alcohol/chitosan sponge and potential applied for wound dressing, European Polymer

Journal. 157 (2021) 110619. https://doi.org/10.1016/j.eurpolymj.2021.110619.

Acknowledgements

CNRS is gratefully acknowledged for funding of this project. We thank Pierre Ilbizian (PERSEE, Mines ParisTech, France) for supercritical drying, Gabriel Monge (CEMEF, Mines ParisTech) for performing XRD experiments and Suzanne Jacomet (CEMEF, Mines ParisTech) for help with SEM imaging. Declarations of interest: none.

Tuning the properties of porous chitosan: aerogels and cryogels Coraline Chartier, Sytze Buwalda, Hélène Van Den Berghe, Benjamin Nottelet, Tatiana Budtova