Joël Legrand
email: joel.legrand@loria.fr

Yannick Toussaint

Chedy Raïssi

Adrien Coulet

Syntax-based Transfer Learning for the Task of Biomedical Relation Extraction

Transfer learning (TL) proposes to enhance machine learning performance on a problem, by reusing labeled data originally designed for a related problem. In particular, domain adaptation consists, for a specific task, in reusing training data developed for the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because those usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains. In this paper, we experiment with TL for the task of Relation Extraction (RE) from biomedical texts, using the TreeLSTM model. We empirically show the impact of TreeLSTM alone and with domain adaptation by obtaining better performances than the state of the art on two biomedical RE tasks and equal performances for two others, for which few annotated data are available. Furthermore, we propose an analysis of the role that syntactic features may play in TL for RE.

Introduction

A bottleneck problem for training deep learningbased architecture on text is the availability of large enough annotated training corpora. This is especially an issue in highly specialized domains such as those of biomedicine. TL approaches address this problem by leveraging existing labeled data originally designed for related tasks or domains [START_REF] Weiss | A survey of transfer learning[END_REF]. However, adaptation between dissimilar domains may lead to negative transfer, i.e. transfer that decreases the performance for the target domain. In this article, we apply a TL strategy using the TreeLSTM model for the task of biomedical Relation Extraction (RE). We propose an analysis of the syntactic features of source and target domain corpora to provide elements of interpretation for the improvements we obtained. Relation Extraction (RE) aims at identifying in raw and unstructured text all the instances of a predefined set of relations between identified entities. A relationship takes the form of an edge between two or more named entities as illustrated in Figure 1. We are considering here binary RE that can be seen as a classification task by computing a score for each possible relation type, given a sentence and two identified entities.

Deep learning methods have demonstrated good ability for RE [START_REF] Zeng | Relation classification via convolutional deep neural network[END_REF], but one of their drawbacks is that, in order to obtain reasonable performances, they generally require a large amount of training data, i.e., text corpora where entities and relationships between them are annotated. The assembly of this kind of domain-and task-specific corpora, such as those of interest in biomedicine, is time consuming and expensive because it involves complex entities (e.g., genomic variations, complex phenotypes), complex relationships (which may be hypothetical, contextualized, negated, n-ary) and requires trained annotators. This explains why only few and relatively small (i.e., few hundreds of sentences) corpora are available for some biomedical RE tasks, making these resources particularly valuable. Distinct approaches, such as TL or distant supervision [START_REF] Mintz | Distant supervision for relation extraction without labeled data[END_REF] have been particularly explored to overcome this limit. With the latter approach, existing relationships available in knowledge-or data-bases are used to enrich the training set, with-out considering more labeled corpora .

Domain adaptation is a type of TL that allows taking advantage of data annotated for a source domain to improve the performances in a related target domain [START_REF] Weiss | A survey of transfer learning[END_REF]. However, even if the source and target domain share the same language (i.e., English), thus a common syntax, TL between domains may lead to negative transfer since specific source domains may use specific vocabularies as well as specific formulations that are inadequate to the target domain. Hence, we need to better understand and characterize what makes a source corpus potentially helpful, or harmful, with regard to a RE task.

The contribution of this paper is twofold. First, we show that, compared to a baseline Convolutional Neural Network (CNN)-based model, a syntax-based model (i.e., the TreeLSTM model) can better benefit from a TL strategy, even with very dissimilar additional source data. We conduct our experiments with two biomedical RE tasks and relatively small associated corpora, SNPPhenA [START_REF] Bokharaeian | SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature[END_REF] and EU-ADR (van [START_REF] Van Mulligen | The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships[END_REF] as target corpora and three larger RE corpora, Semeval 2013 DDI [START_REF] Herrero-Zazo | The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions[END_REF], ADE-EXT [START_REF] Gurulingappa | Extraction of potential adverse drug events from medical case reports[END_REF], reACE [START_REF] Hachey | Datasets for generic relation extraction[END_REF] as source corpora. Second, we propose a syntax-based analysis, using both quantitative criteria and qualitative observations, to better understand the role of syntactic features in the TL behavior.

Related work

Deep Learning Models for Relation Extraction

Deep learning models, based on continuous word representations have been proposed to overcome the problem of sparsity inherent to NLP [START_REF] Huang | Distributional representations for handling sparsity in supervised sequence-labeling[END_REF]. In [START_REF] Collobert | Natural language processing (almost) from scratch[END_REF], the authors proposed a unified CNN architecture to tackle various NLP problems traditionally handled with statistical approaches. They obtained stateof-the-art performances for several tasks, while avoiding the hand design of task specific features. [START_REF] Zeng | Relation classification via convolutional deep neural network[END_REF] showed that CNN models can also be applied to RE. In this study, they learn a vectorial sentence representation, by applying a CNN model over word and word position embeddings, which is used to feed a softmax classifier [START_REF] Bishop | Pattern recognition and machine learning[END_REF]. To improve the performance of RE, authors, such as [START_REF] Xu | Semantic relation classification via convolutional neural networks with simple negative sampling[END_REF] and [START_REF] Yang | A position encoding convolutional neural network based on dependency tree for relation classification[END_REF], consider elements of syntax within the embedding provided to the model.

Beside CNN models that incorporate syntactic knowledge in their embeddings, other approaches proposed neural networks (NN) in which the topology is adapted to the syntactic structure of the sentence. In particular, Recursive Neural Network (RNN) have been proposed to adapt to tree structures resulting from constituency parsing [START_REF] Socher | Parsing with compositional vector grammars[END_REF][START_REF] Legrand | Joint RNNbased greedy parsing and word composition[END_REF]. In this vein, [START_REF] Sheng | Improved semantic representations from tree-structured long short-term memory networks[END_REF] introduced a TreeLSTM, a generalization of LSTM for treestructured network topologies, which allows processing trees with arbitrary branching factors.

The first model to use RNN for RE was proposed by [START_REF] Liu | A dependency-based neural network for relation classification[END_REF]. The authors introduced a CNN-based model applied on the shortest dependency path between two entities, augmented with a RNN-based feature designed to model subtrees attached to the shortest path. [START_REF] Miwa | End-to-end relation extraction using LSTMs on sequences and tree structures[END_REF] introduced a variant of the TreeLSTM that allows, like the model used in this paper, to take the whole dependency tree into account and not only the shortest path between two entities.

In this paper, we compare two deep learning strategies for RE: (1) the MultiChannel CNN (MCCNN) model [START_REF] Quan | Multichannel Convolutional Neural Network for Biological Relation Extraction[END_REF], which has been successfully applied to the task of proteinprotein interaction extraction without using any syntactic feature as input and (2) the TreeLSTM model [START_REF] Sheng | Improved semantic representations from tree-structured long short-term memory networks[END_REF], which is designed for considering dependency trees. These two models are detailed in section 3.

Transfer learning

TL allows to overcome the lack of training data for a given target task by transferring knowledge from source data not originally designed for that purpose [START_REF] Weiss | A survey of transfer learning[END_REF]. One can distinguish multitask learning in which performances on a given task are improved using information contained in the training signals of auxiliary related tasks [START_REF] Caruana | Multitask learning[END_REF] from domain adaptation in which only one task is considered but its application domains differ [START_REF] Ben-David | A theory of learning from different domains[END_REF]. While the former is a form of inductive transfer in which the auxiliary task introduces an inductive bias during training, the latter is a form of transductive transfer. such as [START_REF] Plank | Embedding semantic similarity in tree kernels for domain adaptation of relation extraction[END_REF] focusing on unsupervised domain adaptation (i.e., without any labeled target data) and deep learning based ones such as [START_REF] Fu | Domain adaptation for relation extraction with domain adversarial neural network[END_REF][START_REF] Zhao | Multiple source domain adaptation with adversarial training of neural networks[END_REF] focusing on domain adversarial learning (an approach which ensures that the feature distributions over the source and target domains are made similar using an extra domain classifier at train time). Differently, our approach is a case of multi-source domain adaptation (i.e., implying that we have labeled data, both in target and source corpora) and does not involve adversarial training.

Negative transfer occurs when the information learned from a source domain and task has a negative impact on the performances of the target task. Despite the fact that negative transfer is a major issue in TL, to our knowledge only few works have been conducted to overcome this problem [START_REF] Weiss | A survey of transfer learning[END_REF]. Most of them use a relatedness metrics to select the elements of the source that are the most related to the target. For instance, [START_REF] Seah | Combating negative transfer from predictive distribution differences[END_REF] defined a positive transferability measure that allows removing irrelevant source data. Ge et al. ( 2014) also focused on domain adaptation from multiple sources. They proposed a method to avoid negative learning caused by unrelated or irrelevant source domains, using a weighting mechanism based on a relatedness metrics between the source and target data.

In this work, we experiment with a domain adaptation method on the RE task using the TreeL- STM model, with relatively small biomedical corpora as target corpora and, larger biomedical or general domain corpora as source corpora. We also provide elements of interpretation of the impact of syntactic dependency structures on TL. In this matter, and unlike [START_REF] Seah | Combating negative transfer from predictive distribution differences[END_REF] or Ge et al. ( 2014), the relatedness measures used in this work emphasizes the key role of syntax in TL with TreeLSTM.

Models

We compare in this article the performances of the MCCNN and TreeLSTM models. Both models compute a fixed-size vector representation for a whole sentence by composing input embeddings. A score is computed for each possible type of relationship (e.g., negative, positive or speculative) between two identified entities.

In this section, we first introduce the embedding input layer, which is common to both approaches (i.e., MCCNN and TreeLSTM); Then, we detail how each approach composes sequences of embedding in order to compute a unique vectorial sentence representation; Finally, we present the scoring layer, which is common to both approaches.

Input layer

Both models are fed with word embeddings (i.e., continuous vectors) of dimension d w , along with extra entity embeddings of size d e . These embeddings are concatenated to form the input of the model. Formally, given a sentence of N words, w 1 , w 2 , . . . , w N , each word w i ∈ W is first embedded in a d w -dimensional vector space by ap- plying a lookup-table operation: LT W (w i ) = W w i , where the matrix W ∈ R dw×|W| represents the parameters to be trained in this lookuptable layer. The dictionary W is composed of all the words of the given corpus. Each column W w i ∈ R dw corresponds to the vector embedding of the w i th word in our dictionary W.

Besides, entity embeddings (coming from a simple 3-elements dictionary) enable to distinguish between words which compose either the first entity, the second entity or are not part of any entity. They are respectively called first entity, second entity and other embeddings. Finally, word and entity embeddings are concatenated to form the input corresponding to a given word. Let's denote x i the concatenated input corresponding to the i th word.

Composition layers

Both models take the embeddings as input and output a fixed-size representation r s of size d s , which corresponds to the whole sentence with two identified entities. Accordingly, one sentence with more than two entities will lead to one embedding for each pair of entities. This section details the two models used in this study.

MCCNN

The MCCNN models applies a variable kernel size CNN to multiple input channels of word embeddings. Inspired by the three-channel RGB image processing models, it considers different embedding channels (i.e., different word embeddings versions for each word) allowing to capture different aspects of input words.

More formally, given an input sequence x 1 , . . . , x N , applying a kernel to the i th window of size k is done using the following formula:

C = h( N -k+1 j=1 W [x i , . . . , x i+k-1 ] j + b)
where [ ] j denotes the concatenation of inputs from channel j, W ∈ R (dw+de)×d h and b ∈ R d h are the parameters, d h is the size of the hidden layer, h is a pointwise non-linear function such as the hyperbolic tangent and c is the number of input channels. For each kernel, a fixed size representation r h ∈ R d h is then obtained by applying a max-pooling over time (here, the time means the position in the sentence).: r h = max C

We denote K the number of kernels with different sizes. A sentence representation r s ∈ R ds (with d s = K * d h ) is finally obtained by concatenating the output corresponding to the K kernels r s = [r 1 h , . . . , r k h ] , where r k h corresponds to the output of the k th kernel. Figure 2 illustrates the structure of a two-channel CNN, with two kernels of size 2 and 3, on a four-words sentence.

TreeLSTM

The TreeLSTM model, and more specifically its Child-Sum version, [START_REF] Sheng | Improved semantic representations from tree-structured long short-term memory networks[END_REF] processes the dependency tree associated with an input sentence in a bottom-up manner. This model is suitable for processing dependency trees since it handles trees with arbitrary branching factors and no order between children of a node. This is done by recursively processing the nodes of the tree, using at each iteration, the representations of the children of the current node as input. The transition function for a node j and a set of children C(j) can be found in the original paper (Tai et al., 2015) using x j ∈ R dw+de as input for node j. The TreeL-STM outputs a sentence representation r s ∈ R ds corresponding to the output state o j of the top tree node (i.e., the root node of the dependency tree that spans all the others). Figure 3 illustrates the structure of the TreeLSTM computed for a fourwords sentence.

Scoring layer

Both the MCCNN and TreeLSTM models output a unique vector representation r s ∈ R ds that takes the entire sentence into account, as well as two identified entities. This representation is used to feed a single layer NN classifier, which outputs a score vector with one score for each possible type of relationship. This vector is obtained using the formula: s(r s ) = W (s) r s + b (s) , where W (s) ∈ R ds×|S| and b (s) ∈ R |S| are the trained parameters of the scorer, |S| is the number of possible relation types. The scores are interpreted as probabilities using a softmax layer [START_REF] Bishop | Pattern recognition and machine learning[END_REF].

Datasets

We explore how RE tasks that focus on a type of relationship associated with scarce resources may take advantage from larger corpora developed for distinct domains. To this purpose, we selected (i) two small target biomedical corpora and (ii) three larger source corpora. All are publicly available and detailed in the following section. Table 3 summarizes their main characteristics.

Target corpora

SNPPhenA [START_REF] Bokharaeian | SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature[END_REF]) is a corpus of abstracts of biomedical publications, obtained from PubMed (Fiorini et al., 2017), annotated with two types of entities: single nucleotide polymorphisms (SNPs) and phenotypes. Relationships between them are annotated and classified in 3 types: positive, negative and neutral. EU-ADR (van Mulligen et al., 2012) is a corpus of PubMed abstracts annotated with drugs, diseases and drug targets (proteins/genes or gene variants) entities. It is composed of 3 subcorpora of 100 abstracts each, encompassing annotations of either target-disease, target-drug or drug-disease relationships. Annotated relationships are classified in 3 types: positive, speculative and negative associations (PA, SA and NA respectively). In [START_REF] Bravo | Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research[END_REF], performances are assessed over the TRUE class, which is composed of the PA, SA and NA types, in contrast with the FALSE class.

Source corpora

SemEval 2013 DDI (Drug-Drug Interaction) [START_REF] Herrero-Zazo | The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions[END_REF] consists of texts from DrugBank and MEDLINE annotated with drugs. Drug are categorized in 4 categories: drug, brand, group and drug n (i.e., active substances not approved for human use). Relationships are classified in 4 types: mechanism, effect, advice and int (default category, when no detail is provided). ADE-EXT (Adverse Drug Effect corpus, extended) [START_REF] Gurulingappa | Extraction of potential adverse drug events from medical case reports[END_REF] consists of MEDLINE case reports, annotated with drugs and conditions (e.g., diseases, signs and symptoms), along with untyped relationships between them. reACE (Edinburgh Regularized Automatic Content Extraction) [START_REF] Hachey | Datasets for generic relation extraction[END_REF] consists of English broadcast news and newswire annotated with organization, person, fvw (facility, vehicle or weapon) and gpl (geographical, political or location) entities along with relationships between them. Relationships are classified in five types: general-affiliation, organisation-affiliation, partwhole, personal-social and agent-artifact.

Experiments

Training and Experimental Settings

Our models were trained by minimizing the loglikelihood over the training data. All parameters (weights, biases and embeddings) were iteratively updated via backpropagation for the MCCNN and backpropagation Through Structure [START_REF] Goller | Learning task-dependent distributed representations by backpropagation through structure[END_REF] for the TreeL-STM. Hyper-parameters were tuned using a 10fold cross-validation by selecting the values leading to the best averaged performance, and fixed for the remaining experiments. Word embeddings were pre-trained on ˜3.4 million PubMed abstracts (corresponding to all those published between Jan. 1, 2014 and Dec. 31, 2016) using the method described in [START_REF] Lebret | Word embeddings through hellinger PCA[END_REF].

MCCNN model. Following [START_REF] Kim | Convolutional neural networks for sentence classification[END_REF] both channels were initialized with pre-trained word embeddings, but gradients were backpropagated only through one of the channels.

Hyperparameters were fixed to d w = 100, d e = 10, d h = 100 for each of the 2 channels, d s = 2 × d h = 200. We used two kernels of size 3 and 5 respectively. We applied a dropout regularization after the embedding layers [START_REF] Srivastava | Dropout: a simple way to prevent neural networks from overfitting[END_REF] with a dropout probability fixed to 0.25. Table 2: Results of our TL strategy in terms of precision (P), recall (R) and f-measure (F). σ F is the standard deviation of the f-measure. The + in the column Train corpus indicates that we trained our model using the target corpus plus one additional source corpus.

TreeLSTM model. Dependency trees were derived from parsing trees obtained using the Charniak-Johnson parser trained on GENIA and PubMed data [START_REF] Mcclosky | Selftraining for biomedical parsing[END_REF]. Hyper-parameters were fixed to d w = 100, d e = 10, d h = 200 and d s = 200. We applied a dropout regularization after every TreeLSTM unit and after the embedding layers. The dropout probability was fixed to 0.25. All the parameters are initialized randomly except the word embeddings.

We evaluated performances in terms of precision (P), recall (R) and f-measure (F). For multilabel classifications, we report the macro-average performance 1 . For SNPPhenA, we performed a cross-validation using 10% of the corpus for the validation and the provided test corpus for testing (which is about 30% the size of the training cor-1 The macro-average metric is less impacted by classes with few test instances (and thus a high variance). For this reason, it is more representative of the performance of our model. pus). Because no test corpus is provided with EU-ADR, we performed a 10-fold cross-validation using 10% of the corpus for the validation and 10% for the test of our models.

Transfer learning experiment

In this subsection, we present our TL strategy and its results. Following a standard practice in deep learning, the transfer learning is done by training models in parallel while using shared representations, as illustrated by [START_REF] Collobert | Natural language processing (almost) from scratch[END_REF]. In other terms, for each experiment, the same network, initialized with random weights, is used for each corpus (i.e., same embedding layer and TreeLSTM weights), except for the scorer, which is adapted to each corpus as the number and types of relationships may change. During the training phase, using a standard stochastic gradient descent procedure [START_REF] Robbins | A stochastic approximation method[END_REF], we randomly pick training sentences from the mixed corpus (i.e., target + one source training corpora). Table 3: Performance comparison between the state of the art [START_REF] Bokharaeian | SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature[END_REF][START_REF] Bravo | Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research[END_REF] and this work in terms of precision (P), recall (R) and F-measure (F). Results reported for this work are ensembles of the 5 best models obtained.

Figure 4: Dependency parse tree of a sentence from SNPPhena expressing a relation between the entities rs429358 and dementia. The shortest dependency path between the two entities is shown in bold.

This training procedure is done, starting from different random initialization for each fold of our cross-validation. Table 2 presents the results of the TL study. Each results is an average of 100 experiment (10 experiments for each fold starting from different random initialization). We observed that for the TreeLSTM model, additional source corpora consistently improved the performances. More interestingly, this phenomenon occurs even for corpora of distinct types of entities such as the combination of SNPPhenA and Se-mEval 2013 DDI and, to a lesser extend, with the corpus that is outside of the biomedical domain, reACE. We note that the pre-trained embeddings were obtained using biomedical sources. This may affect the TL performance with reACE that is not of the biomedical domain. Also, we did not observed any benefit of the TL strategy for the MCCNN model, which performances decrease slightly in comparison with the baseline experiments.

Comparison with the state of the art

Table 3 presents a comparison of performances obtained with our approach versus two state-ofthe-art systems applied to the RE tasks associated respectively with SNPPhenA [START_REF] Bokharaeian | SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature[END_REF] and EU-ADR [START_REF] Bravo | Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research[END_REF].

Our results are obtained using, for each fold, an ensemble of the 5 best models for this fold, according to the validation. The ensembling was done by averaging the scores s(r s ) of each individual model, following [START_REF] Legrand | Joint RNNbased greedy parsing and word composition[END_REF]. We report the 10-folds average performance. Both state-of-the-art systems use a combination of a shallow linguistic kernel with a kernel that exploits deep syntactic features. Our approach outperforms the performances reported for SNPPhenA and one EU-ADR subtasks and lead to similar performances for the two remaining EU-ADR subtasks.

6 On the role of syntactic features in transfer learning

Empirical results suggest that the TreeLSTM model is more positively-influenced by syntactic similarity between source and target corpora than by domain closeness. Indeed, the TreeLSTM model explicitly includes the syntactic structure of the sentences in the network topology. Thus, a source corpus, such as reACE, that share neither entity nor vocabulary with the target corpus proved to be helpful. We propose in the following an analysis of the role of the syntactic features. We also provide real examples illustrating similarities between corpora and comment them. Syntactic features. We propose three comparisons based on patterns extracted from shortest paths between two entities in dependency graphs. Shortest path proved to be effective for RE [START_REF] Razvan | A shortest path dependency kernel for relation extraction[END_REF][START_REF] Cellier | Sequential patterns to discover and characterise biological relations[END_REF]. From a shortest path (as between rs429358 and dementia in Figure 4), we extract 3 different patterns. The first one is made with the partof-speech (POS) and dependency tags (DT): for example, in Figure 4, "NN nsubj *JJ* nmod NN nmod NN"2 . The second and the third patterns are built by keeping only either the POS or the DT. The patterns associated with our running example are then: "NN *JJ* NN NN" and "nsubj ** nmod nmod". For a given pattern, the syntactic similarity score is obtained using the following procedure: Given 2 corpora, (1) we first extract all the shortest path pattern that appear between two related entities. (2) For each corpus, we compute the pattern distribution (i.e., the list of patterns, along with their frequency) by normalizing over all the patterns in the corpus. (3) The score is then computed with the cosine similarity between the pattern distributions of two corpora. Table 4 shows the cosine similarity measures between target and source corpora for the three different pattern distributions. We observe that, for the two target corpora, the performance gain obtained using the TL strategy using a given source corpus can be related to the cosine similarity with this corpus: the higher cosine similarity lead to the best transfer TL. Dictionary coverage. On the opposite, we observed that the efficiency of TL in our experiments can not be fully explained by the lexical similarity between source and target corpora. As shown in Table 5, the vocabulary overlap with the target corpora is almost equivalent whether we are considering DDI or ADE (53.4 vs. 51.2 and 58.9 vs. 60.5), whereas performances obtained with DDI were better than those obtained with ADE. Unsurprisingly, it is lower for reACE which is not a biomedical corpus. Lexical and semantic paradigms. We complete this analysis with few examples illustrating the lexical and semantic heterogeneity of sentences that may instantiate a same pattern. Table 6 provides 4 patterns and their instantiations in source and target corpora. One can observe that sentences instantiating a same pattern seems to have no particular similarity when considering lexical and semantic paradigms. A similar heterogeneity is observed when considering the lowest common ancestor term (or the head) of the patterns. Table 7 lists the most frequent lowest common ancestor in each corpus. Again, we observe no direct link with learning improvement.

Conclusion

In this paper, we empirically showed that a TL strategy can benefit biomedical RE tasks when using the TreeLSTM model, whereas it is mainly harmful with a model that does not consider syntax. This is of great interest for specific domains, such those of biomedicine, for which few annotated resources are available. Our TL approach led (i) to better performances than the state of the art for two biomedical RE tasks: SNPphenotype and drug-disease RE; and (ii) to stateof-the-art results for two others focusing on targetdisease and target-drug relationships. Interestingly, we showed that even a general domain corpus (reACE) may carry useful information and lead to improved performances. We proposed an analysis with syntax-based metrics and examples to provide elements of interpretation of this behavior and emphasize the key role of syntax in TL for RE. An exciting direction would be to explore this transfer strategy with Electronic Health Records of various origin.

Aknowledgement

This work is funded by the French National Research Agency (ANR) under the PractiKPharma project: ANR-15-CE23-0028, by the IDEX "Lorraine Université d'Excellence" (15-IDEX-0004) and by the Snowball Inria Associate Team.

Figure 1 :

 1 Figure 1: Example of relationship typed as Weak Confidence Association between two named entities: a SNP (single nucleotide polymorphism) and a Phenotype, from the SNPPhenA corpus.

Figure 3 :

 3 Figure 3: The TreeLSTM model. Each node takes as input the representation of its children. Red words correspond to the entities.

Table 1 :

 1 Main characteristics of our target and source corpora. Two corpora are divided into subcorpora. The sizes of the training and test corpora are reported in term of number of sentences (sent.) and annotated relationships (rel.). EU-ADR, ADR-EXT and reACE have no proper test corpus.

		Corpus	Subcorpus	Train Size	Test Size	#Entity #Relation
		name		sent.	rel.	sent. rel.	Types	Types
		SNPPhenA	-	362	935	121	365	2	3
	Target corpora		drug-disease	244	176			4	3
		EU-ADR	drug-target	247	310	-	-	4	3
			target-disease	355	262			4	3
		SemEval	DrugBank	5,675 3,805	973	889	4	4
	Source corpora	2013 DDI ADE-EXT	MEDLINE -	1,301 5,939 6,701 232	326 -	95 -	4 2	4 1
		reACE	-	5,984 2,486	-	-	4	5

Table 4 :

 4 Cosine similarity score between target and source corpora for the three different pattern distributions. POS is part of speech pattern and DT is dependency type pattern.

			Source corpora
			DDI ADE reACE
				POS + DT
		SNPPhena 0.53	0.22	0.13
	Source corpora	EU-ADR SNPPhena 0.80 0.24 EU-ADR 0.77 SNPPhena 0.53	0.20 POS only 0.70 0.68 DT only 0.23	0.09 0.35 0.32 0.14
		EU-ADR	0.25	0.24	0.10

Table 5 :

 5 Dictionary coverage. Percentage of words from the target copora present in the source corpora.

		DDI ADE reACE
	SNPPhenA 53.4 51.2	39.8
	EU-ADR	58.9 60.5	38.3

Table 6 :

 6 Examples of patterns and of their instantiation in corpora. Red words correspond to entities.

	Pattern	Corpus	Example of instantiation
	*NN*	SNPPhenA
	conj
	NN
		DDI
	ppmod	EU-ADR
	*NN*
	ppmod
	NN
	ppmod
	NN	DDI
	NN	SNPPhenA
	pmod
	*NN*
	ppmod
	NN	reACE
	NN	SNPPhenA
	dep
	*VBN*
	ppmod NN	reACE
		SNPPhenA		EU-ADR	DDI	ADE	reACE
		associated (25.2)	analyzed (5.8)	entity (17.8)	entity (30.1)	entity (60.6)
		entity (12.2)		associated (4.3)	administered (4.1) developed (11.1)	is (2.2)
		genotyped (5.4)	entity (2.9)	increase (3.0)	associated (4.1)	was (1.9)
		association (4.4)	is (2.9)	administration (2.7)	is (2.7)	said (1.4)
		showed (3.8)	polymorphisms (2.4)	reported (2.6)	induced (2.3)
		observed (3.3) over-represented (2.4)	interact (2.6)	case (1.6)
		genes (2.6)		showed (2.4)	reduce (2.5)	following (1.4)

Table 7 :

 7 Terms corresponding to the lowest common ancestor in the POS + DT patterns. Their relative frequency in each corpus is provided in parenthesis. Entity means that the term is one of the two entities.

The stars mark the lowest common ancestor of the two entities in the dependency tree and are used to prevent similar pattern with different common ancestors to be considered the same. Note that the patterns are not directed, thus the two patterns "NN nsubj *JJ* nmod NN nmod NN" and "NN nmod NN nmod *JJ* nsubj NN" are equivalent.


Hilary Mcmillan
email: hmcmillan@sdsu.edu

Ida Westerberg

Flora Branger

Five Guidelines for Selecting Hydrological Signatures

Introduction

Hydrological signatures are index values derived from observed or modelled series of hydrological data such as rainfall, flow or soil moisture. They are designed to extract relevant information about hydrological behaviour, such as to identify dominant processes, and to determine the strength, speed and spatiotemporal variability of the rainfall-runoff response.

A signature is often scalar; common examples of signatures calculated from streamflow include mean flow, baseflow index or slope of the Flow Duration Curve (FDC). Signature indices have long been used in hydrological analyses, but the concept of a hydrological signature was first explicitly described by [START_REF] Gupta | Reconciling theory with observations: elements of a diagnostic approach to model evaluation[END_REF] in the context of deriving minimal representations of the relevant information contained in a hydrological dataset.

Hydrological signatures have many applications, for example: learning about hydrological processes in a given location, comparing observed hydrological behaviour against theoretical or modelled behaviour, and determining hydrological similarity across time or space. However, the link between signature values and hydrological behaviour and processes is not always straightforward, leading to uncertainty and variability in hydrologists' signature choices.

Most studies use a selection of different signatures to capture different aspects of the catchment response, for example evaluating overall flow distribution as well as high and low flow extremes and flow timing. Such studies may choose their own signatures, or borrow subsets of signatures used in other works. There is little agreement regarding guidelines or requirements for successful choices of signatures or sets of signatures. We believe that reaching a consensus on selection criteria for hydrological signatures will assist hydrologists in choosing between competing signatures and facilitate comparison between hydrological studies.

The aim of this paper is to encourage a more rigorous approach to hydrological signature selection, which considers the ability of signatures to represent hydrological behaviour and underlying processes for the catchment and application in question. To this end, we propose a set of guidelines for selecting hydrological signatures. We describe five criteria that any hydrological signature should conform to: Identifiability, Robustness, Consistency, Representativeness, and Discriminatory Power. We explain why each criterion is important and give examples of design or adaption of signatures to meet the guidelines.

Signature Selection Review

Any study using hydrological signatures requires the hydrologist to choose one or more appropriate signatures. The reasons given by authors (including ourselves!) are varied, but typically belong to one of the following categories.

(1) A set or subset of those signatures used in a previous study; for example: "signatures utilised in previous studies" (Coxon, Freer, Wagener, Odoni, & Clark, 2014, p. 6139).

(2) Signatures designed to capture a broad range of hydrological information. For example: "signature information of interest for a wide range of applications and […] across a range of flow behaviour" [START_REF] Westerberg | Uncertainty in hydrological signatures for gauged and ungauged catchments[END_REF](Westerberg et al., , p. 1850)), "to capture independent information about catchment behaviour at annual, seasonal and daily timescales, and to capture average as well as extreme hydrological behaviour" (Sawicz et al., 2014, p. 274), "constructed to reflect different aspects of the hydrograph" [START_REF] Euser | A framework to assess the realism of model structures using hydrological signatures[END_REF](Euser et al., , p. 1896).

(3) A set of signatures that are independent or uncorrelated from one another; for example: "the response characteristics [were] checked for redundancy" [START_REF] Yadav | Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins[END_REF], p. 1762) (4) Signatures that can be related to hydrological function; for example: "those […] that have an interpretable link to catchment function" [START_REF] Sawicz | Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA[END_REF], p. 2897), "signature patterns of behaviour that are related to the primary watershed functions" and "capable of extracting useful information pertaining to system functioning" (Yilmaz, Gupta, & Wagener, 2008, p. 7).

We note that in several other studies we reviewed, hydrological signature choices were simply stated with little or no reasons given for the choice.

Guidelines for Selecting Hydrological Signatures

We argue that the design or choice of any signature should be carefully evaluated in terms of its ability to serve the study aim, such as understanding dominant upstream processes or diagnostic model evaluation. Therefore, we propose a set of five guidelines outlined below, and suggest that an acceptable signature should satisfy all or most of these. If choosing between two competing signatures that seek to describe the same process or characteristic, the guidelines will promote a rigorous comparison. Signature users may need to revise such guidelines depending on the main purpose of their analysis, but we believe that a structured evaluation of this type will lead to more robust outcomes.

(1) Identifiability. Uncertainty in the signature value should be small when compared to the range of values between different catchments, ensuring that the uncertainty does not obscure inter-catchment differences [START_REF] Westerberg | Uncertainty in hydrological signatures for gauged and ungauged catchments[END_REF]. An uncertainty estimate should be cited alongside all signature values, whether calculated from the data or estimated from the literature. Signatures can sometimes be designed to be robust to known data uncertainties (Westerberg & McMillan, 2015).

(2) Robustness. Signature values should be independent of the peculiarities of the data collection design. This includes instrument type, exact measurement procedure, length of time series, dates of record, etc. The robustness criterion ensures that the signature describes the catchment itself rather than the measurement of the catchment. For example, this criterion is highly relevant to soil moisture signatures, which should ideally be independent of soil moisture probe type and quirks of installation.

(3) Consistency. Signature values should be comparable across catchments, and insensitive to irrelevant factors. This characteristic can frequently be into the signature design, such as a normalisation by catchment area, to maximise the usefulness of the signature in comparing catchments according to underlying hydrological function rather than superficial catchment characteristics.

(4) Representativeness. Hydrological properties are frequently heterogeneous at small scales, whereas modelling or management applications require estimates of emergent, catchment-scale behaviour. Therefore signatures should describe average (sub-)catchment behaviour where this is meaningful. Signatures based on local properties (e.g. soil moisture or water table level) should preferably not be sensitive to the exact location of the sensor below the (sub-)catchment scale. Signatures based on river flow (e.g. flow dynamics or chemical properties) should represent average upstream tributary behaviour, or users should be aware of the limitation if this is not the case. This implies that signature values should vary approximately linearly with the underlying descriptor, for example, as described by Kirchner (2016a[START_REF] Kirchner | Aggregation in environmental systems-Part 1: Seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments[END_REF] for signatures that use annual isotope cycle amplitude to describe water transit time. Similar concerns apply to signatures describing flood peaks as they merge along the river network.

(5) Discriminatory power. This criterion specifies that the knowledge of signature values should increase our knowledge of hydrological function. Therefore, differences in signature values should increase with differences in hydrological function. Catchments which the hydrologist identifies as functionally similar should return similar signature values. Discriminatory power is also used in a modelling-specific sense, such that a constraint on the signature must reduce the prediction range of feasible models [START_REF] Schaefli | Snow hydrology signatures for model identification within a limits-ofacceptability approach[END_REF]. [START_REF] Yilmaz | A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model[END_REF] further required that a signature relates only to a subset of model parameters; they perturbed each parameter in turn to test the signature sensitivity.

An example of signature design

In this section, we describe an example of the design process for a signature, assessing possible signature designs against the guidelines above. Due to their ubiquity, we chose to consider a signature related to the Flow Duration Curve, selecting the FDC mid-section slope. The majority of hydrological studies using signatures include some aspects of the FDC, to characterize the magnitude and duration of observed flows, and "to evaluate the overall behaviour of a catchment" [START_REF] Euser | A framework to assess the realism of model structures using hydrological signatures[END_REF](Euser et al., , p. 1899)). As described by Yilmaz et al. (2008, p. 8), the FDC in general, and specifically the mid-section slope, can be related to catchment function as it is sensitive to vertical redistribution of soil water, i.e. the partitioning of water between quick flow and slow flow.

Using the slope of the FDC mid-section in linear or log space is common; for example in the range 0.2 to 0.7 [START_REF] Yilmaz | A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model[END_REF] or 0.33 to 0.66 [START_REF] Yadav | Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins[END_REF]. Alternatively, the absolute values of the flow duration curve at a defined set of exceedance probabilities may be used [START_REF] Westerberg | Calibration of hydrologic models using flow-duration curves[END_REF] or a generalisation using the Nash-Sutcliffe Efficiency fit between the two FDCs of interest [START_REF] Euser | A framework to assess the realism of model structures using hydrological signatures[END_REF].

For this example, we consider FDCs, and specifically the FDC mid-section slope, as a proposed signature to quantify catchment overall behaviour and flashiness and evaluate it against each guideline. Specifically, we used the slope in the interval 0.33 to 0.66, in log space, normalised by median flow. Where we find deficiencies, we suggest alternative signatures that may better conform to the guidelines.

(1) Identifiability. FDCs aggregate streamflow values over time, reducing their sensitivity to one-off errors (e.g. a blocked or malfunctioning flow gauge) and therefore improving the identifiability of the hydrological behaviour described by the FDC. However, because FDCs summarise streamflow, they are sensitive to errors in the rating curve function used to derive flow from a measured stage series. These errors can be significant, particularly for floods and low flows (McMillan & Westerberg, 2015), and errors in slope of the rating curve translate directly into errors in slope of the FDC. By using the 0.33-0.66 range only, this signature will have lower uncertainty than those that include the extremes of the FDC. [START_REF] Westerberg | Uncertainty in hydrological signatures for gauged and ungauged catchments[END_REF] found that high uncertainties are also linked to uncertainty in rating curve breakpoints, and that FDC slope uncertainty was high compared to inter-catchment variability, leading to poor identifiability of inter-catchment differences.

(2) Robustness. FDCs are thought to represent stable characteristics of a catchment, and might therefore be expected to be robust to the length of time series or period of record. However, as demonstrated by [START_REF] Vogel | Flow-duration curves. I: New interpretation and confidence intervals[END_REF], even 30-year FDCs can be sensitive to the period of record, for example depending on whether any prolonged drought occurred during the period. They therefore recommend using the average annual FDC rather than the total FDC. The midsection slope signature is more robust to the period of record than the low-flow section of the FDC (e.g. exceedance probability > 0.7; see Vogel and Fennessey's Fig 6). However, using the midsection slope of the average annual FDC could still be considered in drought-prone areas or if this signature is to be used in combination with low-flow signatures.

(3) Consistency. FDCs are often normalised by mean flow, median flow or catchment area. Multiplicative normalisations do not change the slope of the FDC in log space; however, they are relevant where absolute values of the FDC are compared. Whether or not scaling is recommended depends on the application. If the signature is used for model calibration, scaling by mean or median flow is not recommended as it would obscure bias in flow volumes. However, if the signature is used to compare water partitioning between quick/slow flow in different size catchments, scaling will be necessary.

(4) Representativeness. If we use the mid-section FDC slope as a signature to characterise catchment processes (e.g. flashiness, vertical re-distribution of water); we should consider whether the signature does or should represent an average of these process characteristics in the upstream tributaries. For example, suppose two tributaries meet, of equal annual flow volume, but one is dominated by ephemeral quick flow ("Flashy"), and the other by slow flow ("Not Flashy"). This example is illustrated in Figure 1 using a synthetic example based on two tributaries that each exhibit exponential recession behaviour, but with different time constants.

As shown in Figure 1b, the midsection slope (33% to 66%) of the confluence FDC is low, identical to that of the slow tributary, and is not influenced by the fast tributary. Therefore, this signature would characterise the entire catchment as "Not Flashy", even though half the flow volume comes from a "Flashy" catchment. This result derives directly from the definition of our signature, which only characterises flow at middle range values, after the flashy portion of the hydrograph has already passed. Care is therefore needed in interpreting the signature value, which represents accumulated flow behaviour rather than average upstream behaviour. The signature could be used in conjunction with other signatures targeting the high flow portion of the FDC, as is commonly done (e.g. [START_REF] Yilmaz | A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model[END_REF]. Furthermore, we advise consideration of catchment-specific factors that can impact on hydrograph shape and therefore FDC signature values and their link to runoff generation processes. These include channel-based changes in the hydrograph shape not related to upstream averaging including diffusion of the flood wave, joining of tributaries with different flood timing, and heterogeneous influx of groundwater along the river network. (5) Discriminatory power. Using a similar synthetic example as above, we can see that two equal volume rivers with the same FDC midsection slope signature value can have visually very different flow behaviour, especially for high flows (Figure 2). These two rivers would be characterised as having identical flashiness/vertical water distribution if using only the midsection slope signature. Hence, the signature comparison may disagree with expert opinion on the similarity of the rivers. We may therefore need to revise our initial concept of which types of catchment behaviour are quantified by the FDC midsection slope, for example by explicitly distinguishing between surface and subsurface FDCs (e.g. [START_REF] Yokoo | Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis[END_REF], or by using this signature in combination with a high-flow FDC signature as suggested above. Synthesis. We found that the commonly used midsection slope of the FDC is, under some circumstances, limited in its ability to represent average upstream behaviour (representativeness) or to distinguish between visually different flow series (discriminatory power). While the issues are likely to be less severe in real catchments with more complex flow behaviour, this example demonstrates that this signature does not provide an overall picture of flashiness and vertical water distribution throughout the upstream catchment. Instead, it should be carefully interpreted as representing accumulated behaviour only during the mid-range flows/mid-recession period, and should be used alongside other signatures that target the fast-flow components of the catchment response.

Discussion and Conclusions

The aim of this commentary is to draw the attention of the hydrological community to the importance of signature choice, and the current lack of consensus as to preferred evaluation criteria for signatures. We would welcome discussion of how best to select hydrological signatures, setting the benefits of careful and consistent signature choice against the wide variety of signature applications.

The concept of "discriminatory power" could be considered the most important guideline for signature choice, but this criterion has particular challenges. This can be described using a "chicken and egg" analogy: when testing discriminatory power, how do we know that two catchments have similar or different behaviour without using signatures to test it? We could approach this difficulty by comparing signature values with "soft" or "expert" knowledge of the similarities and differences between catchment behaviours. Alternatively, signature values could be analysed in experimental catchments where auxiliary data provide an independent source of information about the catchment behaviour, e.g. direct measurements of vertical water re-distribution to compare with FDC signature values.

More generally, when designing signatures for a specific catchment, we must think critically about factors that can impact the link between signature values and hydrological processes, using information about catchment configuration, spatial heterogeneity, human impacts, etc. For example, the FDC signatures considered in this paper might present difficulties for signature design, because the FDC shape combines the effects of multiple hydrological processes, for example, mid-range flow values could result from a combination of small flow peaks and late recession times from large flow peaks.

A secondary challenge that is not investigated in this commentary, but forms a companion question, is to discuss guidelines for selecting a "complete set" of signatures for future applications. This task relates to many of the signature selection statements reviewed in Section 2, such as an attempt to select signatures that are independent, or that cover a wide range of hydrological behaviours.

In conclusion, we have proposed five guidelines that could be used to evaluate potential formulations of hydrological signatures. Using the example of the FDC, we demonstrated how assessment against each guideline could be used to compare or choose between alternative signature definitions, depending on the intended application.

Figure 1 :

 1 Figure 1: Example of the merging of two tributaries with different recession behaviours. (a) Characteristic recession curves for each tributary and their combination, (b) flow duration curves for the same rivers, normalised by median flow, and (c) Example flow series for each river, generated bycumulative distribution function (CDF)-correction of an arbitrary flow record.

Figure 2 :

 2 Figure 2: Example flow series of two rivers with identical flow volumes and flow duration curve midsection slope (interval 0.33 to 0.66, in log space, normalised by median flow).

Acknowledgements

IW acknowledges the financial support of The Swedish Research Council Formas (942-2015-321).