Jane Wottawa
email: [wottawa@limsi.fr

Djegdjiga Amazouz
email: amazouz@limsi.fr

Martine Adda-Decker

Lori Lamel
email: lamel]@limsi.fr

Studying vowel variation in French-Algerian Arabic code-switched speech

Keywords: Vowel variation, Code-switched speech, French, Algerian Arabic

Algerian Arabic-French bilinguals show phonetic variation with respect to vowel timber in both their languages. Our study aims to automatically identify vowel variants frequently produced by such bilinguals. To that end, the speech corpus FACST, containing French and Algerian Arabic code-switched speech, was analyzed. A second corpus with native French speakers (NCCFr) was used to provide a reference baseline and to compare vowel variants across the two speaker groups. Three experiments were carried out: first, the French speech of both corpora was aligned with a French acoustic model including parallel nearest-neighbor vowel variants in its pronunciation dictionary. Second, the Arabic speech was aligned using the same acoustic model with parallel vowel variants in its dictionary. Finally, we tested whether peripheral vowels in Algerian Arabic-French bilinguals are more often centralized than in French native speech by allowing schwa as a competing variant. The results show that French natives and Algerian Arabic-French bilinguals globally have a comparable amount of vowel variation in French. However, French natives have stable high vowels whereas bilinguals tend to produce stable low and back vowels. In the centralization experiment, Algerian bilinguals favor the centralization of mid, open and back vowels.

Introduction

It has been shown that bilingual speakers' speech presents more acoustic variation than speech of monolinguals [START_REF] Bullock | Phonetic reflexes of code-switching[END_REF][START_REF] Auer | Code-switching in conversation: Language, interaction and identity[END_REF]. This is explained by the fact that bilinguals have access to more than one phonemic inventory which may lead to potential interferences [START_REF] Fricke | Phonetic variation in bilingual speech: A lens for studying the productioncomprehension link[END_REF][START_REF] Grosjean | A psycholinguistic approach to code-switching: The recognition of guest words by bilinguals[END_REF]. The proposed study focuses on French-Algerian code-switched speech, with bilingual speakers having access to both French and Arabic phonemic inventories. French features a much higher number of vowels than does Algerian Arabic. This makes it particularly interesting to study code-switched (CS) speech [START_REF] Isurin | Multidisciplinary approaches to code switching[END_REF] and investigate how the increase in complexity of the vowel inventory induced by CS is handled by our bilingual Algerian speakers of French and Arabic. To this end, we propose to compare French vowel production variation in Algerian Arabic-French bilinguals and in French (FR) native speakers. Furthermore, we will compare their French productions (FR-Alg) to their speech productions of Algerian Arabic (AA) in CS context [START_REF] Vu | An investigation of codeswitching attitude dependent language modeling[END_REF][START_REF] Laurent | Investigating techniques for low resource conversational speech recognition[END_REF]. The aim of the study is to shed some light on the pronunciation variation Algerian Arabic-French bilinguals produce in both languages and in CS speech. To measure vowel production variation we make use of an automatic speech alignment system which includes vowel-specific pronunciation variants in its pronunciation lexicon. The general idea is the following: if during the alignment a vowel is frequently replaced by one of the proposed competing vowels, this is considered as an indicator of variation.

In our study, we focus on parallel variants [START_REF] Adda-Decker | Pronunciation variants across system configuration, language and speaking style[END_REF][START_REF] Mareüil | Studying pronunciation variants in French by using alignment techniques[END_REF], but do not address phoneme insertion or deletion. Parallel variants present changes in the sound chain which allow multiple choices at a given position. For example, the word série [seri] (series) could have [siri] as a parallel variant if [i] is one of the possible variants of [e].

The remainder of this paper is as follows. First a brief comparison between the FR and AA vowel systems is provided, followed by a presentation of the speech corpora used in this investigation. Next, the methodology is presented followed by the results and their discussion. The study ends with general conclusions and perspectives for future research.

French and Algerian Arabic vowels

Nowadays, the French vowel system tends to be described using eleven oral vowels and three nasal vowels [START_REF] Fougeron | Illustrations of the IPA: French[END_REF]. Referring to the spelling and transliteration of AA [START_REF] Harrat | An Algerian dialect: Study and resources[END_REF][START_REF] Amazouz | The French-Algerian Code-Switching Triggered audio corpus (FACST)[END_REF], the language counts six oral vowels contrasting in duration (3 short vowels, 3 long vowels). The AA vowels are [i, a, u, i:, a:, u:]. The comparison of the two vowel systems shows that French has a richer vocalic system than AA. The eleven oral vowels of French are [i, e, E, y, ø, oe, @, a, O, o, u]. French also has the following three nasal vowels [Ẽ, Ã, Õ], and in some regional varieties, a fourth nasal vowel can be found ([ oe]). Besides the six oral vowels of Modern Standard Arabic, AA is often described with an additional long vowel [e:] [START_REF] Saadane | A conventional orthography for Algerian Arabic[END_REF]. However, this vowel has not yet been introduced in our pronunciation lexicon because of its missing grapheme correspondence in the Arabic orthography.

Speech material

In this study the French Algerian Code-switching Triggered (FACST) corpus of French and Algerian Arabic speech from Algerian Arabic-French bilinguals was used. Details of this corpus which contains both read and spontaneous speech can be found in [START_REF] Amazouz | The French-Algerian Code-Switching Triggered audio corpus (FACST)[END_REF][START_REF] Amazouz | Addressing Code-Switching in French/Algerian Arabic Speech[END_REF]. The corpus contains a total of 7:30h of read and stimulated spontaneous speech with code-switching [START_REF] Kheder | Processing Code-Switching in Algerian Bilinguals: Effects of Language Use and Semantic Expectancy[END_REF][START_REF] ¸etinoglu | A Code-Switching Corpus of Turkish-German Conversations[END_REF] in both French and Algerian Arabic of 20 speakers (10 female). To date the data from 11 speakers, ca. 3 hours has been fully transcribed and verified and is used in the present study.

Furthermore, the Nijmegen Corpus of Casual French (NCCFr) is also used in the analyses in order to provide a reference baseline for vowel variation in French spontaneous speech. The corpus contains about 36h of conversational French of 46 speakers (24 female) raised in Central/Northern France. More detailed information about this corpus can be found in [START_REF] Torreira | The Nijmegen corpus of casual French[END_REF].

Methodology

This section describes the methodology used to study vowel variants via automatic speech-text alignments with lexicons including pronunciation variants for 3 series of experiments with targeted contrasts.

Automatic alignment

The automatic alignment of the parallel vowel variants across both speech corpora and for each one of the three experiments was realized using a set of position-independent monophone acoustic models similar to those described in [START_REF] Gauvain | The LIMSI broadcast news transcription system[END_REF][START_REF] Lamel | Speech transcription in multiple languages[END_REF][START_REF] Gelly | Language recognition for dialects and closely related languages[END_REF][START_REF] Lamel | Automatic speechto-text transcription in Arabic[END_REF]. This setup was used as previous studies showed that since the large sets of context-dependent acoustic models typically used in speech recognition systems, capture some of the coarticulatory variation, there is less of a need for the system to select a different phoneme than the canonical one [START_REF] Adda-Decker | Pronunciation variants across system configuration, language and speaking style[END_REF][START_REF] Mareüil | Studying pronunciation variants in French by using alignment techniques[END_REF]. The alignment system locates word and phone boundaries using the orthographic transcriptions and the best matching pronunciations chosen among the pronunciation variants that are included in its dictionary. Typical pronunciation variants in French are due to optional liaison consonants and schwa vowels (which may be described as sequential variants), allowing for one more or one less phone symbol in the pronunciation. Other typical variants are due to word-final consonant cluster simplifications as in the word autre (other) which provides the following choices to the system: [otr], [Otr], [otr@], [Otr@], [ot], [Ot]. This particular example combines both parallel ([o,O]) and sequential (optional [r] and [@]) variants. However, in general, most lexical entries tend to be described by their canonical (full form) pronunciation.

This method is more objective than human transcriptions however the vowel choices are categorical. In order to capture gradual variations, complementary analyses carried out by humans are necessary.

Production variation quantification

Production variation is quantified for each target vowel by measuring the rates of the corresponding competing vowels that are selected during the automatic alignment phase. For instance, if all occurrences of a given target vowel were aligned unchanged without making use of any of the competing variants, the variation rate would be 0%. In the result section, for each experiment, the variation will be described with the help of variant rates indicating the proportion of occurrences of a target vowel that were replaced by a competing vowel.

Vowel variants in French: French natives vs. bilinguals

In the first experiment, we start measuring vowel production variation using the proposed method with spontaneous speech of French native speakers to establish a reference. Then, we apply exactly the same method to the French and Algerian speech parts of our bilingual speakers [START_REF] White | An investigation of acoustic models for multilingual code-switching[END_REF][START_REF] Van Der Westhuizen | Automatic speech recognition of English-isiZulu code-switched speech from South African soap operas[END_REF], however our analyses focus on the French productions. Production variation is measured for five target vowels, where each target vowel is put in parallel with two competing variants. A specific pronunciation variant lexicon is built for each condition. The defined target vowels are the peripheral vowels [i, e, a, o, u] with competing variants as shown in Table 1. For each target vowel, competing variants were chosen as the two nearest neighbors towards the center of the vocalic space (triangle) with respect to the target vowel's location. For the open vowel [a], we tested two different configurations, termed as anterior and posterior. We also included /i/ as a variant for /e/, and /y/ as a variant for /u/. Our initial analyses indicate that there is no significant change to the results without these extra variants.

The different pronunciation variant lexicons were tested on both corpora (French native NCCFr and the French speech subset of the bilingual FACST corpus) using the LIMSI automatic alignment system with French native acoustic models. In the following, only speech from the FACST corpus was used. Here, we focus on vowel production variation in bilingual speakers as a function of the spoken language, French (FR) or Algerian Arabic (AA). We limit the investigations to the three cardinal vowels [i,a,u] which are shared by the two phonological vowel systems. For each of the three target vowels, the same competing vowels are used as in the first experiment. The automatic alignment system was still based on French acoustic models. As the French inventory contains more vowels, the French acoustic models allow us to quantify what happens in a larger number of smaller vocalic locations in the Algerian Arabic vowel space. It is noteworthy to remind that the use of French acoustic models should not lead to interpretations such as aligned variants correspond to realizations of French phonemes, but rather that the realization of an Arabic vowel is acoustically close to that of French vowels.

Vowel centralization in French and code-switching

In this experiment, we aim at quantifying whether the acoustic realizations of the peripheral vowels tend to move towards the center by simply testing one single competing vowel, the [@] schwa. We also added the three nasal vowels: [i, e, E, a, O, o, u, Ẽ, Ã, Õ] and the following AA vowels i, a, u, i: ,a: ,u:. Considering [@] schwa as the competing variant informs us about vowel reduction, which is also linked to vowel centralization [START_REF] Delattre | An acoustic and articulatory study of vowel reduction in four languages[END_REF]. This experiment allows us to measure whether the schwa variant is particularly productive in a CS context [START_REF] Khattab | Phonetic accommodation in children's codeswitching[END_REF] and whether vowels tend to become more centralized.

Results

This section reports results for the 3 series of experiments aiming at quantifying vowel production variation, either by contrasting vowel substitution or by centralization for native French and bilingual Algerian-French speakers. Variant rates by vowel, by group/language as well as by group/language for each vowel were analyzed using the Chi-square test of independence. displayed as they turned out to be the most productive in native French and to produce globally similar results as the posterior variants in bilingual French.

Vowel variants in French: French natives vs. bilinguals

Statistical analyses show no significant difference of variation across vowels (χ 2 (10) = 14.07, p = 0.17). All vowel targets are equally likely to allow variants. Furthermore, both groups produce a comparable amount of vowel variants (χ 2 (2) = 0.64, p = 0.73). In French, French-Algerian bilinguals are as likely as French native speakers to produce vowel variants. With respect to differences in vowel variant rates by group for each vowel, we observe significant differences only for the [a] target allowing anterior variants (χ 2 (2) = 9.76, p < 0.01). In this vowel condition, French-Algerian bilinguals produce the [a] target significantly more often (74.3 %) than do French native speakers (54.2 %).

Finally, it is interesting to note that for the 3 vowels [i,a,u], the bilingual speakers have much larger variant rates in their native Algerian speech than in French. The following subsection sheds more light on variations between FR-Alg and AA speech.

Vowel variants in code-switching

In read speech, the tested variants show that vowels are less stable in AA than in FR-Alg speech produced by Algerian Arabic-French bilinguals. Statistical analyses show that in read speech the variation rates differ according to the vowel (χ 2 (6) = 23.37, p < 0.001). For the target vowel [i] the first variant ([e] in our case) was chosen more often (31.6 %) than the other target vowels. Inversely, the first variant ([O]) of the target vowel [a] (post) was chosen significantly less often (9.5 %) compared to the first variant of the other target vowels. Furthermore, variation rates depend on the language the French-Algerian Arabic bilinguals are speaking (χ 2 (2) = 19.19, p < 0.001). While reading in French, the bilingual speakers produce the target vowel significantly more often (77.1 %) than while reading in Algerian Arabic (47.5 %), leading to higher variation rates for the AA speech. With respect to variation rates by language for each vowel, we only observe no significant differences for the target vowel [u] (χ 2 (2) = 1.25, p = 0.54). For the target vowel [i], results show that the bilinguals differ regarding the variant [e] (χ 2 (2) = 15.22, p < 0.001). In French, the variant [e] was chosen only in 18.8 % of the cases whereas in Algerian Arabic 44.1 % of the [i] targets were aligned as [e]. With respect to the [a] target with both

Vowel centralization in French and code-switching

As shown in Table 2, for French native speakers, the oral vowel [i] as well as the three nasal vowels [Ẽ, Ã, Õ] were least affected by vowel centralization.

Statistical analyses confirmed that vowel centralization in French is vowel dependent (χ 2 (9) = 28.24, p < 0.001). The vowel [O] is most affected by vowel centralization (29.8 %) For the AA part of the study, vowel centralization has been studied in a slightly different way because the vowels [e] and [o] do not have a grapheme correspondence in Arabic. Thus we decided to include the following short and long Arabic vowels in the analysis [i, i:, a, a:, u, u:]. The alignment was still carried out with the French acoustic model used for the other experiments. Results are summarized in Table 3. They show that globally long vowels are less often centralized than short vowels. This tendency was confirmed in both speech styles reading and CS.

Globally, the centralization of AA vowels are more noticeable in the short vowels in read speech the CS speech precisely the gap between the front vowel [i] with 56.5% and its long version with 15.0%. We conclude, the duration of vowels gives more stability to realization and in AA the hyper-articulation in medium and speed speech rate for the read speech boosts the centralization and schwa production. However, this tendency was not confirmed by the statistical analyses (χ 2 (1) = 1.01, p = 0.32).

Statistical analyses indicated that vowel centralization is also vowel dependent in Algerian Arabic (χ 2 (5) = 30.00, p < 0.001). The vowel [i:] is less often centralized (17.4 %) than the other vowels i.e. [i, a, a:, u, u:] (39.2 %). However, speech style i.e. reading and CS do not have an impact on vowel centralization in Algerian Arabic (χ 2 (5) = 7.67, p = 0.18).

Discussion and Conclusions

In this study we examined vowel variants of [i, e, a, o, u] as well as vowel centralization in French and Algerian Arabic. With respect to French, we compared two speaker populations: French natives and Algerian-Arabic bilinguals. The Arabic speech was exclusively produced by Algerian-Arabic bilinguals.

Our results showed that, overall, Algerian Arabic-French bilinguals produce as much variability of vowels in French as do French native speakers. There is only one exception to this observation: in French, Algerian Arabic-French bilinguals show less variation for [a] targets than do French native speakers. Algerian Arabic-French bilinguals seem to vary less on low vowels than high vowels. Which leads us to the conclusion that the bilinguals do not reproduce the difference between high and low vowels observed in French native speakers regarding the degree of variation.

French-Algerian Arabic bilinguals showed more variability across all target vowels in Arabic than they did in French. This result suggests that these bilinguals have different production strategies according to the language.The French vowel triangle shows a higher density than does the Arabic vowel triangle. The bilingual speakers in our study adapt to the language and vary their vowels accordingly.

In French natives, the low variability observed for high vowels, especially [i], might be explained by the density of the vowel triangle, notably for the high frontal vowels [i, y, e, E], which share a restricted place, hence, low variability of these vowels helps communication. In Algerian Arabic however, as presented above, only three phonological vowels exist /i, a, u/, which each present a short/long duration difference. However, phonetically Algerian Arabic also has the mid front vowel [e:]. The variability observed for [i] in French and Arabic in our data suggests that this mid front vowel is rather a variant of /i/ than /a/ for these Algerian Arabic-French bilinguals.

Finally, with respect to the parallel variants with [@] in French, our data showed that the vowel [O] is more often centralized compared to the other target vowels. Similar findings were reported in [START_REF] Boula De Mareüil | Accents étrangers et régionaux en franc ¸ais[END_REF] where [O] was found to be close to [oe] which is also a central vowel. Our data in French showed, that French-Algerian Arabic bilinguals centralize vowels in the same way as do French native speakers. However, descriptively we find that in French natives, whose vowel productions are generally more variable, the most stable vowels are [i, e] and the three nasal vowels, whereas in Algerian Arabic-French bilinguals the most stable vowels, next to the nasals, were [i, a, u]. The mid vowels [e, E, O, o] were frequently aligned with [@]. This might be explained with the Algerian Arabic phonemic vowel system where mid vowels are absent and thus not very important for successful communication. The vowels [i, a, u], on the other hand are important on a phonemic level for Arabic speakers because from a structural point of view Arabic distinguishes for instance word classes verb tenses over vowel variation in similar consonant contexts, hence, vowel variation with a mutual central vowel could be fatal for communication and sentence structure.

With respect to Arabic, our results showed that the target [i:] is less often centralized than the other vowels. A possible reason for this result might lay in the extreme position of [i:] in the vowel triangle. In order to investigate this hypothesis, further acoustic analyses are needed.

In conclusion, our corpus based analysis suggests that French-Algerian Arabic bilinguals are able to vary their vowel productions according to the language they speak, thereby adjusting their productions to the respective vowel systems.

Figure 1

 1 Figure1shows the vowel variant results as a function of the five vowel targets [i,e,a,o,u]. Variant rates are stacked starting from the bottom with the target vowel to which are added the two competing vowels. For each target vowel, the first stack of results corresponds to the French natives (as a reference) before the results of the French CS speech of the bilinguals are displayed. For the [a] target vowel, only the anterior variants are

Figure 1 :

 1 Figure 1: Expt 1: Vowel variants in native and code-switched French. For each target vowel on the x-axis, variant rates of native French (FR) speakers are compared to those of Algerian French (FR-Alg). Variant rates are stacked and sum up to 100%. [a] target results are shown for the anterior condition.

Figure 2 :

 2 Figure 2: Expt 2: Vowel variants for bilingual Algerian-French speakers on the French (FR) and Arabic (AA) speech subsets of FACST CS data. types of variants (ante: (χ 2 (2) = 50.76, p < 0.001), post: (χ 2 (2) = 36.31, p < 0.001)), the bilinguals produce a high number of targets in French ([a] (ante): 86.0 %, [a] (post): 87.3 %) compared to their productions in Algerian Arabic ([a] (ante): 37.0 %, [a] (post): 48.6 %). As a consequence, in Algerian Arabic, variant rates for the anterior variants [E] (25.0 %) and [oe] (38.0 %) are significantly higher than in French ([E]: 6.2 %, [oe]: 7.8 %). With respect to the posterior variants for the target [a], only the variant [oe] appears more often in Algerian Arabic (38.7.0 %) than in French (6.4 %). CS speech is rather different from read speech especially for the AA part of the data. In FR-Alg, we observe that the target vowels were aligned as [i, a, u] in most of the cases in both read and code-switched speech. Only the low score of [u] in read speech is an exception to that tendency. Statistical analyses show that vowel variation rate in CS speech is vowel dependant (χ 2 (6) = 25.52, p < 0.001). Compared to the other target vowels, [i] allows the first variant ([e] in our case) more often (35.3 %). Inversely, for the target [a] with posterior variants, the first vowel variant [O] is produced less frequently (10.7 %) compared to the first variant of the other vowels. Moreover, language also has an impact on the vowel variant rates (χ 2 (2) = 12.96, p < 0.01). Overall, French-Algerian bilinguals produce the target vowel more often in French (63.0 %) than they do in Algerian Arabic (41.6 %). Detailed analyses for each target vowel and its variants revealed that French-Algerian bilinguals significantly modify their speech production according to the language ([i]: (χ 2 (2) = 10.18, p < 0.01); [a] with anterior variants: (χ 2 (2) = 21.04, p < 0.001); [a] with posterior variants: (χ 2 (2) = 12.92, p < 0.01); [u]: (χ 2 (2) = 13.97, p < 0.001)). The bilinguals produced significantly more target vowels speaking French for [i] (57.5 %), [a] with anterior variants (74.3 %) and [u] (63.0 %)than while speaking Algerian Arabic ([i]: 35.9 %, [a] with anterior variants: 42.9 % and [u]: 37.5 %). Regarding the target vowel [a] with posterior variants, the bilinguals substituted [a] more often by [oe] while speaking Algerian Arabic (39.0 %) than while speaking French (16.7 %).

Table 1 :

 1 Competing vowel variants for each target vowel.The last column exemplifies the effect on the pronunciation lexicon. Note that for the most open [a] vowel, two sets of variants (ante, post) are proposed.

	Target vowel	Competing variants	Example
	[i]	[e, y]	lit (bed) : li, le, ly
	[e]	[E, oe]	nez (nose) : ne, nE noe
	[a]	[E, oe] [O, oe]	chat (cat) : Sa, SE, Soe (anterior) (cat) : Sa, SO, Soe (posterior)
	[o]	[O, ø]	chaud (hot) : So, SO, Sø
	[u]	[o, ø]	loup (wolve) : lu, lo, lø
	4.4. Vowel variants in code-switching

Table 2 :

 2 Expt 3. Vowel centralization measured as schwa variant rates (%) for native and Algerian speakers of French. The last column displays the difference between FR and FR-Alg.

	Vowel	FR	FR-Alg	∆
	i	14.1	12.8	1.3
	e	20.9	24.4	-3.5
	E	34.1	15.9	14.2
	a	34.0	15.9	14.1
	O	39.4	20.2	19.2
	o	33.5	21.6	11.9
	u	25.0	16.2	8.8
	Ẽ	13.6	7.7	5.9
	ã	17.5	8.7	8.8
	Õ	17.7	6.5	11.2

Table 3 :

 3 Vowel centralization (%) for bilingual French-Algerian speakers in Arabic read and converational CS speech.

	Vowel	i	i:	a	a:	u	u:
	Reading 56.5 15.0 42.4 26.8 44.7 24.0
	CS	37.9 19.7 49.0 36.4 41.1 33.0
	whereas the vowel [Ẽ] is least affected by centralization
	(10.7 %). With respect to group (FR, FR-Alg), no significant
	differences regarding vowel centralization are found (χ 2 (9) =
	9.50, p = 0.39).

Acknowledgements

This work is supported by the French National Agency for Research as part of the SALSA project (Speech and Language technologies for Security Applications) project under grant ANR-14-CE28-0021 and by the French Investissements d'Avenir -Labex EFL program (ANR-10-LABX-0083).


Johannes Liem
email: johannes.liem@city.ac.uk

Eirini Goudarouli

Steven Hirschorn

Jo Wood
email: j.d.wood@city.ac.uk

Charles Perin
email: cperin@uvic.ca

Conveying Uncertainty in Archived War Diaries with GeoBlobs

Keywords:

We introduce GeoBlob, an abstract representation of spatio-temporal data dedicated to conveying uncertain positions and uncertain temporal information of entities that move over time. We describe the preliminary design space of GeoBlobs by exploring variations of the technique to visualize data extracted from hand-written World War One diaries, which is our driving context and motivation for this project.

INTRODUCTION AND BACKGROUND

Since 2014, The National Archives (UK) have been digitizing analog, hand-written War Diaries from the First World War (WWI), documenting the story of the British Army and its units on the Western Front. Using the crowdsourcing platform Operation War Diary [START_REF]Operation War Diary[END_REF] (OWD), "citizen historians" tag 1.5 million scanned and archived pages. They classified and annotated essential information on each diary page, generating extensive time-series data about military units, including labels for casualties, unit strength, weather, everyday army life, military activities, soldier names and ranks, location names and dates. As part of a project between City, University of London and The National Archives that aims to communicate the "life behind the trenches" [START_REF] Grayson | A Life in the Trenches? The Use of Operation War Diary and Crowdsourcing Methods to Provide an Understanding of the British Army's Day-to-Day Life on the Western Front[END_REF], this poster focusses on the visual representation of the spatio-temporal data derived from the War Diaries.

The digitizing and tagging process introduces uncertainty on many levels due to missing records, misspellings (in original diaries and while digitizing), unreadable parts, lost diaries, the reliability of workers, and also through post-processing of the gathered data. As a result, misspellings and ambiguities during geo-referencing of mentioned place names can result in noisy and cluttered map displays when using common visualizations of movement such as line symbols (Figure 1). Furthermore, locations at which the unit did not reside at the time are also mentioned in the diaries but not identifiable as such (e.g., When a soldier was reported to go on furlough to his hometown only the place name but not the context was digitally collected). Additionally, the data typically do not capture the exact temporal order of locations on a single day.

Instead of giving the illusion that the data is complete and clean, our approach is to leverage this uncertainty to produce a more "organic" view of a unit's movement over time.

GEOBLOB DESIGN SPACE

Our aim was to design visual representations of military units that incorporate and acknowledge uncertainty or noise in the data. We started out by exploring possible geometries to visualize a unit's movement during the war.

We define GeoBlobs as an abstract representation of spatiotemporal data dedicated to conveying uncertain positions and uncertain temporal information of entities that move over time. Instead of showing an entity at a given point in time, GeoBlobs convey an unordered estimation of the possible locations over a temporal window using enclosed shapes. Many different parameters can influence the design of GeoBlobs. We defined and explored the following design variations in the context of the OWD data:

Temporal Window. A start and end date define a window for temporal aggregation of the included locations (Figure 2). Sliding the window on the temporal axis animates the GeoBlob over time.

Spatial Window. We apply a distance heuristics to weight each location within the temporal window and adjust the set of locations that form the GeoBlob (Figure 2). This helps to filter outliers (e.g., unlikely locations that have an incorrect geo-location). The temporal and the spatial window define the size and shape of the GeoBlob. Keeping both windows constant over time, the size and shape of the geometry convey information about the speed of a unit. While narrow and long shapes imply a greater distance covered (e.g., Figure 3 like GeoBlobs consider the temporal order of locations. Buffers for nodes and edges can influence the appearance of a GeoBlob; we distinguish between wide, narrow and mixed buffers (Figure 4 bottom). Style Parameters. Different visual variables, including color, transparency, focus (blur effect), pattern, or gradient fill can be used to vary the visual style of a GeoBlob's stroke or fill (Figure 5). Especially using a blurry or sketchy [START_REF] Wood | Sketchy Rendering for Information Visualization[END_REF] style can help to visually convey the uncertainty of the data. Motion. When animating a GeoBlob, its motion encodes the overall direction of the displayed army unit. Here the temporal and spatial window might introduce a certain generalization level (e.g. back and forth movements within a larger temporal window are not obvious), which on the other hand reflects the uncertainty in the data. We also investigate how motion can be applied to convey additional information like a shaking GeoBlob on fighting days (Figure 6a).

Contextualization. Besides motion, outline and fill overlays can provide context. For example by visually integrating unit activities like fighting, re-supplying the front, and resting behind the lines or events like famous battles (Figure 6b).

Multiple Shapes. Mapping multiple entities (i.e., army units) concurrently allows to compare their individual movements (e.g., two units were located at the same front, but split after a battle) (Figure 6c). Generating multiple layers for a single GeoBlob taking the different location probabilities into account and using transparency allows to convey the uncertainty of the locations (Figures 6d and3).

PROTOTYPE

We implemented a browser-based prototype using the mapping framework leaflet [1] with custom D3 SVG overlays [START_REF]D3SvgOverlay: Leaflet Plugin: D3 SVG Overlay[END_REF] to visu- alize the OWD data. To calculate the geometry for the GeoBlobs, we make use of the Bubble Sets algorithm [START_REF] Collins | Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations[END_REF].

A set of sliders allows to explore and vary the visualization design space. It includes interactive adjustment of the temporal window (how many days are visualized by a unit GeoBlob), spatial window (the distance from a blobs centroid to remove outlier locations forming the GeoBlob), blob properties (e.g.,size or style), playback speed, or duration and fade out time of overlaid events.

DISCUSSION AND CONCLUSION

GeoBlobs are an abstract representation of moving entities on a map over time with uncertain positions. In contrast to the visual certainty conveyed by crisp line or dot visualizations, GeoBlobs leverage the uncertainty in the data to produce an "organic" view of such entities, in this case of UK Army units during WWI. GeoBlobs also suffer less from scalability issues, while line or dot/glyph visualizations become cluttered when the number of data points is large.

GeoBlobs are well suited to any dataset with uncertain locations (e.g., hurricane prediction data). Moreover, the technique can be transfered to domains where the movement data is not uncertain but has a high density or the area of coverage is of interest. For example, GeoBlobs are a promising way of visualizing the movement, the spatial coverage and the pressure in team sports, as well as of visually aggregating spatio-temporal trajectories of players. All are important topics in sports visualization [START_REF] Perin | State of the Art of Sports Data Visualization[END_REF]. Due to its configurable visual appearance we think GeoBlobs can be used in Data Comics [START_REF] Bach | The Emerging Genre of Data Comics[END_REF].

Future work will include the incorporation of additional information from the OWD data and additional data sources about WWI. Which will lead towards a narrative visualization for communicating "the life behind the trenches", one aim of the collaboration between City, University of London and The National Archives. We also plan to run empirical evaluations to assess the strengths and weaknesses of GeoBlobs and their variations.

Figure 1 :

 1 Figure 1: Location data (longitude, latitude) after geo-referencing place names for a single military unit connected in chronological order with lines results in a cluttered map display.

Figure 2 :

 2 Figure 2: Variations of the temporal (red) and spatial (blue) window defining the inclusion of locations for the GeoBlob generation.

  middle), small circular forms indicate local or no movements (e.g., Figure3right).Shape Parameters. We distinguish convex-hull-like, skeletonlike, and graph-like shapes (Figure4top). While skeleton-like geometries connect locations along the shortest distance, graph-

Figure 3 :

 3 Figure 3: Snapshots of browser-based prototype of two units moving from Belgium into Germany after the war in early 1919 with a temporal window of 10 days.

Figure 4 :

 4 Figure 4: Design variations of the shape/form of GeoBlobs.

Figure 5 :

 5 Figure 5: Design variations of stroke and fill style.

Figure 6 :

 6 Figure 6: Contextualization and multiple GeoBlob options.


Retro-digitizing Turkish dictionaries using GROBIDdictionaries 1

Emrah Özcan 2

Dictionaries are invaluable resources about languages. Beginning from the famous Dīwān Lughāt al-Turk, many Turkish dictionaries were printed until now, and today, we still value these resources as they contain the traces of the history of the language, the society's situation at the time of the publication which also, directly or indirectly, reflected the policies imposed upon speakers. With this in mind, different editions of the same dictionary, published at different times, could bring new perspectives at how we look at the language. The fact that today's technology allows us, the researchers, the necessary tools to investigate more about the language we are studying, we need digital versions of dictionaries in order to do analyze deep into those resources; by searching and comparing the content. With the current technological tools in hand, we can deal with huge data as if we can deal with couple of print pages. We need couple of minutes, to do tasks that need days or weeks, if done by hand.

Thus, at this year's Lexical Data Masterclass 2018, I attended the GROBID-dictionaries workshop given by the core developer of the project, Mohamed Khemakhem, to use GROBID-dictionaries for retro-digitizing Turkish dictionaries.

GROBID-dictionaries work with OCRed PDF files. Thus, you need a good OCRed PDF version of the dictionary. Otherwise, PDF2XML engine will fail to recognize the characters or the page layout. So, I recommend you to choose a good OCR software, or try and see whatever options you have.

After providing the PDF, you need a decent machine, with enough RAM to handle the process. Choosing Linux or MacOS as the operating system will help you a lot, as Windows machines come with a bunch of alternations among different editions. You can also install Linux on top of your existing OS via dual boot.

After choosing the OS, you need to install a couple of applications on your machine; oXygen XML Editor and Docker.

After you follow the steps in the given instructions, you may start experimenting with the GROBID-dictionaries by writing this command in you terminal (I will be using MacOS instructions here); docker pull medkhem/grobid-dictionaries When you pull, you will see your terminal downloading the necessary files from Docker container, aka pulling the Docker image. Then, you can get into the GROBID-dictionaries with your first command:

docker run -it medkhem/grobid-dictionaries bash

After you put the above command in your terminal, your prompter will look like this.

root@someRandomNumbersAndLetters: /grobid/grobid-dictionaries# This shows you that you are in the GROBID-dictionaries image now as a user.

GROBID-Dictionaries is based on cascading CRF models. The diagram below presents the architecture enabling the processing and the transfer of the text information through the models.

Cascading CRF models used in GROBID-dictionaries Interestingly, the levels for models in GROBID-Dictionaries reflects the actual levels from the TEI in libraries recommendation. Models can be seen below: These models introduce the system the parts of the page layout in a dictionary and as you proceed within the models, you start annotating.

After annotating the models, the system should be trained with 2/3 of the annotated pages, and the rest of the annotated files, 1/3, should be used for evaluation. Therefore, the system will use the files used as evaluation as a reference point to calculate an f1 score. The higher the score, the better the results are. In other words, if results are calculated less than 97, then more annotated pages are necessary, such as pages with a variety of content, so the system will see more diverged samples and the results will get better after this training.

Here are the results from our training data. The first one is from the first model, Dictionary Segmentation.

Dictionary segmentation f1 score

As can be seen in the above picture, f1 score is 100%. This show that the system was able to understand <body>, <dictScrap>, <footnote> and <headnote> tags correctly.

Next model is Dictionary Body Segmentation, and this model is a little bit less complicated than the first model as it contains only two tags; <entry> and <dictScrap>. Here is the result from the second model:

Dictionary Body Segmentation f1 score

As it can be seen in the screenshot, f1 score for the second model needs more varied annotation that contains <dictScrap>, since the score is 0. As it has been mentioned above, this indicates that the evaluation data contains no <dictScrap> tag, therefore system is unable to train. The solution for this is to add more annotation in the evaluation set that contains the mentioned tag.

Repeated procedure for each model will take place and after each procedures, model should be checked whether it is successful enough or not.

Another important point that needs to be taken care of while using GROBID-dictionaries is the quality of the OCRed PDF file. If the OCRed PDF contains problematic issues, the system will have hard time to understand to recognize the file correctly. This problem will make it harder for the system to work properly as one continues to the next models. As the model number increases, the system looks for more detailed information on the page layout. Therefore, it is advisable to use more than one OCR software to test and see if the OCRed PDF contains problems. Depending on the software, sometimes one type of problem could be seen in another OCR process and the vice versa. In our case, we used Adobe Acrobat and ABBYY FineReader softwares, and each had its own problems with the OCR process. Re-OCRization is needed if different softwares produces different results.

In the third model, lexical entry is being annotated. oXygen software's author mode is used for the annotation process. This mode helps the annotator as the system already provides the necessary CSS and NRG files. In the below picture, the author mode in oXygen can be seen during the annotation process.

oXygen Author mode is used for the annotation process

In case one annotates enough training data and evaluation data for GROBID-dictionaries, thinking that the OCRed PDF is clean, you could retro-digitize a dictionary containing thousands of pages by annotating dozens of pages for each and every model and help you to have a fully TEI compliant digital version of the print dictionary.

Even in cases where it is hard to have a clean OCR PDF, due to the physical appearance of the print dictionary, GROBID-dictionaries can still be used to have just the entries out of the print dictionary. Especially, if the project is only interested in comparing different versions of the print dictionaries, from the perspective of entry selection.

I would like to thank Mohamed Khemakhem for his invaluable help and patience for numerous questions during the GROBID-dictionaries session.

References:

  For the first stage model of processing etymology information (EtymQuote model) o For the second stage model of processing etymology information (Etym model)

• Dictionary Segmentation model, • Dictionary Body Segmentation model, • Lexical Entry model, • Form model, • Sense model, • Etymology model, o

  Sep 2017, Leiden, Netherlands. 〈hal-01508868v2〉

	• Mohamed Khemakhem, Axel Herold, Laurent Romary. Enhancing
	Usability	for	Automatically	Structuring	Digitised
	Dictionaries. GLOBALEX workshop at LREC 2018, May 2018,
	Miyazaki, Japan. 2018. 〈hal-01708137v2〉
	• Mohamed Khemakhem, Luca Foppiano, Laurent Romary.
	Automatic Extraction of TEI Structures in Digitized Lexical
	Resources	using	Conditional	Random	Fields. electronic
	lexicography, eLex 2017,

This paper is presented at the Lexical Data Masterclass

Participants' Symposium, 3-7 December 2018, Berlin, Germany. 2 Yildiz Technical University, Istanbul, Turkey -eozcan@yildiz.edu.tr