Towards Electronic SMS Dictionary Construction: An Alignment-based Approach

International audienceIn this paper, we propose a method for aligning text messages (entitled AlignSMS) in order to automatically build an SMS dictionary. An extract of 100 text messages from the 88milSMS corpus (Panckhurst el al., 2013, 2014) was used as an initial test. More than 90,000 authentic text messages in French were collected from the general public by a group of academics in the south of France in the context of the sud4science project (http://www.sud4science.org). This project is itself part of a vast international SMS data collection project, entitled sms4science (http://www.sms4science.org, Fairon et al. 2006, Cougnon, 2014). After corpus collation, pre-processing and anonymisation (Accorsi et al., 2012, Patel et al., 2013), we discuss how "raw" anonymised text messages can be transcoded into normalised text messages, using a statistical alignment method. The future objective is to set up a hybrid (symbolic/statistic) approach based on both grammar rules and our statistical AlignSMS method

Sud4science, de l'acquisition d'un grand corpus de SMS en français à l'analyse de l'écriture SMS

International audienceThis article describes the sud4science project (www.sud4science.org). Firstly, the authors present the acquisition phase of both SMS data and questionnaire data. Secondly, they explain anonymisation techniques, transcoding and optional annotation phases. Finally, they propose preliminary (socio-) linguistic analyses of scriptural usage of SMS writing, and they also indicate those that are planned in the foreseeable future.Dans le cadre de cet article, on expose le déroulement du projet sud4science (www.sud4science.org). En premier lieu, on décrit la phase d'acquisition des données en provenance des SMS et du questionnaire, avant d'aborder les étapes successives d'anonymisation, de transcodage et d'annotation optionnelle. Ensuite, on présente les analyses (socio-)linguistiques des pratiques scripturales de l'écriture SMS (eSMS) qui ont débuté, ainsi que celles prévues à court et à moyen terme

Données authentiques : un grand corpus de SMS en français

National audienceQu’est-ce que la donnée écrite en sciences du langage ? Trois types se distinguent : 1) la donnée lexicale, qui se présente essentiellement sous forme d’une entrée lexicale, regroupant un ensemble de propriétés ; 2) » le nom spécifique de la donnée observable en linguistique est l’exemple » et renvoie à « un énoncé qui pourrait être effectivement prononcé, même s’il ne l’est pas dans les faits » (Milner 1989, p. 51-52) ; 3) la donnée en tant que texte brut, i.e. le corpus. En linguistique(s) de corpus, il s’agit d’analyser les productions authentiques contenues dans le corpus. Dans certaines écoles linguistiques, au contraire, l’étude du corpus tout-venant n’a pas lieu d’être. Ainsi, perdure le débat concernant l’opposition (ou, tout au moins, la différenciation) entre exemples linguistiques (éventuellement « fabriqués ») et productions authentiques relevées dans des corpus (cf. entre autres, pour le français, Bilger et al. 2000, Cori et al. 2008, Habert et al.1997, Péry-Woodley 1995). En vingt ans, notre propre approche a évolué : d’une analyse linguistique-informatique basée sur l’exemple (Panckhurst 1994, p. 39), nous sommes passée à une analyse de la donnée authentique figurant dans des corpus (Panckhurst 2013, p. 97, Panckhurst et al. 2014). Pour nous, cette mutation s’explique, d’une part, par l’évolution de l’accès aux données, et, d’autre part, par le discours électronique médié (Panckhurst 1997, 2006), circulant entre individus se servant d’outils électroniques (ordinateurs, tablettes, téléphones portables, etc.), qui induit des pratiques et des usages émergents. En deux décennies, la constitution de corpus numérisés ou nativement numériques est devenue monnaie courante, et cette accessibilité massive constitue en soi une nouveauté. Les données authentiques existant sous la forme de courriels, forums, chats, blogs, réseaux sociaux, et, plus récemment de SMS, facilement exploitables par les chercheurs, permettent l’observation, la fouille et l’analyse des pratiques et des usages (novateurs ou non) des scripteurs. Dans le cadre de cette communication, nous expliquerons ce cheminement, en nous focalisant sur des recherches récentes, portant sur le recueil, le traitement et l’analyse d’un grand corpus de SMS en français, intitulé « 88milSMS » (consultable sur la grille de services d’Huma-Num : http://88milsms.huma-num.fr/)

Automating the anonymisation of textual corpora

[EU] Gaur egun, testu berriak etengabe sortzen doaz sare sozialetako mezu, osasun-txosten, dokumentu o zial eta halakoen ondorioz. Hala ere, testuok informazio pertsonala baldin badute, ezin dira ikerkuntzarako edota beste helburutarako baliatu, baldin eta aldez aurretik ez badira anonimizatzen. Anonimizatze hori automatikoki egitea erronka handia da eta askotan hutsetik anotatutako domeinukako datuak behar dira, ez baita arrunta helburutzat ditugun testuinguruetarako anotatutako corpusak izatea. Hala, tesi honek bi helburu ditu: (i) Gaztelaniazko elkarrizketa espontaneoz osatutako corpus anonimizatu berri bat konpilatu eta eskura jartzea, eta (ii) sortutako baliabide hau ustiatzea informazio sentiberaren identi kazio-teknikak aztertzeko, helburu gisa dugun domeinuan testu etiketaturik izan gabe. Hala, lehenengo helburuari lotuta, ES-Port izeneko corpusa sortu dugu. Telekomunikazio-ekoizle batek ahoz laguntza teknikoa ematen duenean sortu diren 1170 elkarrizketa espontaneoek osatzen dute corpusa. Ordezkatze-tekniken bidez anonimizatu da, eta ondorioz emaitza testu irakurgarri eta naturala izan da. Hamaika anonimizazio-kategoria landu dira, eta baita hizkuntzakoak eta hizkuntzatik kanpokoak diren beste zenbait anonimizazio-fenomeno ere, hala nola, kode-aldaketa, barrea, errepikapena, ahoskatze okerrak, eta abar. Bigarren helburuari lotuta, berriz, anonimizatu beharreko informazio sentibera identi katzeko, gordailuan oinarritutako Ikasketa Aktiboa erabili da, honek helburutzat baitu ahalik eta testu anotatu gutxienarekin sailkatzaile ahalik eta onena lortzea. Horretaz gain, emaitzak hobetzeko, eta abiapuntuko hautaketarako eta galderen hautaketarako estrategiak aztertzeko, Ezagutza Transferentzian oinarritutako teknikak ustiatu dira, aldez aurretik anotatuta zegoen corpus txiki bat oinarri hartuta. Emaitzek adierazi dute, lan honetan aukeratutako metodoak egokienak izan direla abiapuntuko hautaketa egiteko eta kontsulta-estrategia gisa iturri eta helburu sailkapenen zalantzak konbinatzeak Ikasketa Aktiboa hobetzen duela, ikaskuntza-kurba bizkorragoak eta sailkapen-errendimendu handiagoak lortuz iterazio gutxiagotan.[EN] A huge amount of new textual data are created day by day through social media posts, health records, official documents, and so on. However, if such resources contain personal data, they cannot be shared for research or other purposes without undergoing proper anonymisation. Automating such task is challenging and often requires labelling in-domain data from scratch since anonymised annotated corpora for the target scenarios are rarely available. This thesis has two main objectives: (i) to compile and provide a new corpus in Spanish with annotated anonymised spontaneous dialogue data, and (ii) to exploit the newly provided resource to investigate techniques for automating the sensitive data identification task, in a setting where initially no annotated data from the target domain are available. Following such aims, first, the ES-Port corpus is presented. It is a compilation of 1170 spontaneous spoken human-human dialogues from calls to the technical support service of a telecommunications provider. The corpus has been anonymised using the substitution technique, which implies the result is a readable natural text, and it contains annotations of eleven different anonymisation categories, as well as some linguistic and extra-linguistic phenomena annotations like code-switching, laughter, repetitions, mispronunciations, and so on. Next, the compiled corpus is used to investigate automatic sensitive data identification within a pool-based Active Learning framework, whose aim is to obtain the best possible classifier having to annotate as little data as possible. In order to improve such setting, Knowledge Transfer techniques from another small available anonymisation annotated corpus are explored for seed selection and query selection strategies. Results show that using the proposed seed selection methods obtain the best seeds on which to initialise the base learner's training and that combining source and target classifiers' uncertainties as query strategy improves the Active Learning process, deriving in steeper learning curves and reaching top classifier performance in fewer iterations

“You’re trolling because…” – A Corpus-based Study of Perceived Trolling and Motive Attribution in the Comment Threads of Three British Political Blogs

This paper investigates the linguistically marked motives that participants attribute to those they call trolls in 991 comment threads of three British political blogs. The study is concerned with how these motives affect the discursive construction of trolling and trolls. Another goal of the paper is to examine whether the mainly emotional motives ascribed to trolls in the academic literature correspond with those that the participants attribute to the alleged trolls in the analysed threads. The paper identifies five broad motives ascribed to trolls: emotional/mental health-related/social reasons, financial gain, political beliefs, being employed by a political body, and unspecified political affiliation. It also points out that depending on these motives, trolling and trolls are constructed in various ways. Finally, the study argues that participants attribute motives to trolls not only to explain their behaviour but also to insult them

The CoMeRe corpus for French: structuring and annotating heterogeneous CMC genres

Final version to Special Issue of JLCL (Journal of Language Technology and Computational Linguistics (JLCL, http://jlcl.org/): BUILDING AND ANNOTATING CORPORA OF COMPUTER-MEDIATED DISCOURSE: Issues and Challenges at the Interface of Corpus and Computational Linguistics (ed. by Michael Beißwenger, Nelleke Oostdijk, Angelika Storrer & Henk van den Heuvel)International audienceThe CoMeRe project aims to build a kernel corpus of different Computer-Mediated Com-munication (CMC) genres with interactions in French as the main language, by assembling interactions stemming from networks such as the Internet or telecommunication, as well as mono and multimodal, synchronous and asynchronous communications. Corpora are assem-bled using a standard, thanks to the TEI (Text Encoding Initiative) format. This implies extending, through a European endeavor, the TEI model of text, in order to encompass the richest and the more complex CMC genres. This paper presents the Interaction Space model. We explain how this model has been encoded within the TEI corpus header and body. The model is then instantiated through the first four corpora we have processed: three corpora where interactions occurred in single-modality environments (text chat, or SMS systems) and a fourth corpus where text chat, email and forum modalities were used simultaneously. The CoMeRe project has two main research perspectives: Discourse Analysis, only alluded to in this paper, and the linguistic study of idiolects occurring in different CMC genres. As NLP algorithms are an indispensable prerequisite for such research, we present our motiva-tions for applying an automatic annotation process to the CoMeRe corpora. Our wish to guarantee generic annotations meant we did not consider any processing beyond morphosyn-tactic labelling, but prioritized the automatic annotation of any freely variant elements within the corpora. We then turn to decisions made concerning which annotations to make for which units and describe the processing pipeline for adding these. All CoMeRe corpora are verified, thanks to a staged quality control process, designed to allow corpora to move from one project phase to the next. Public release of the CoMeRe corpora is a short-term goal: corpora will be integrated into the forthcoming French National Reference Corpus, and disseminated through the national linguistic infrastructure ORTOLANG. We, therefore, highlight issues and decisions made concerning the OpenData perspective

Automating the anonymisation of textual corpora

[EU] Gaur egun, testu berriak etengabe sortzen doaz sare sozialetako mezu, osasun-txosten, dokumentu o zial eta halakoen ondorioz. Hala ere, testuok informazio pertsonala baldin badute, ezin dira ikerkuntzarako edota beste helburutarako baliatu, baldin eta aldez aurretik ez badira anonimizatzen. Anonimizatze hori automatikoki egitea erronka handia da eta askotan hutsetik anotatutako domeinukako datuak behar dira, ez baita arrunta helburutzat ditugun testuinguruetarako anotatutako corpusak izatea. Hala, tesi honek bi helburu ditu: (i) Gaztelaniazko elkarrizketa espontaneoz osatutako corpus anonimizatu berri bat konpilatu eta eskura jartzea, eta (ii) sortutako baliabide hau ustiatzea informazio sentiberaren identi kazio-teknikak aztertzeko, helburu gisa dugun domeinuan testu etiketaturik izan gabe. Hala, lehenengo helburuari lotuta, ES-Port izeneko corpusa sortu dugu. Telekomunikazio-ekoizle batek ahoz laguntza teknikoa ematen duenean sortu diren 1170 elkarrizketa espontaneoek osatzen dute corpusa. Ordezkatze-tekniken bidez anonimizatu da, eta ondorioz emaitza testu irakurgarri eta naturala izan da. Hamaika anonimizazio-kategoria landu dira, eta baita hizkuntzakoak eta hizkuntzatik kanpokoak diren beste zenbait anonimizazio-fenomeno ere, hala nola, kode-aldaketa, barrea, errepikapena, ahoskatze okerrak, eta abar. Bigarren helburuari lotuta, berriz, anonimizatu beharreko informazio sentibera identi katzeko, gordailuan oinarritutako Ikasketa Aktiboa erabili da, honek helburutzat baitu ahalik eta testu anotatu gutxienarekin sailkatzaile ahalik eta onena lortzea. Horretaz gain, emaitzak hobetzeko, eta abiapuntuko hautaketarako eta galderen hautaketarako estrategiak aztertzeko, Ezagutza Transferentzian oinarritutako teknikak ustiatu dira, aldez aurretik anotatuta zegoen corpus txiki bat oinarri hartuta. Emaitzek adierazi dute, lan honetan aukeratutako metodoak egokienak izan direla abiapuntuko hautaketa egiteko eta kontsulta-estrategia gisa iturri eta helburu sailkapenen zalantzak konbinatzeak Ikasketa Aktiboa hobetzen duela, ikaskuntza-kurba bizkorragoak eta sailkapen-errendimendu handiagoak lortuz iterazio gutxiagotan.[EN] A huge amount of new textual data are created day by day through social media posts, health records, official documents, and so on. However, if such resources contain personal data, they cannot be shared for research or other purposes without undergoing proper anonymisation. Automating such task is challenging and often requires labelling in-domain data from scratch since anonymised annotated corpora for the target scenarios are rarely available. This thesis has two main objectives: (i) to compile and provide a new corpus in Spanish with annotated anonymised spontaneous dialogue data, and (ii) to exploit the newly provided resource to investigate techniques for automating the sensitive data identification task, in a setting where initially no annotated data from the target domain are available. Following such aims, first, the ES-Port corpus is presented. It is a compilation of 1170 spontaneous spoken human-human dialogues from calls to the technical support service of a telecommunications provider. The corpus has been anonymised using the substitution technique, which implies the result is a readable natural text, and it contains annotations of eleven different anonymisation categories, as well as some linguistic and extra-linguistic phenomena annotations like code-switching, laughter, repetitions, mispronunciations, and so on. Next, the compiled corpus is used to investigate automatic sensitive data identification within a pool-based Active Learning framework, whose aim is to obtain the best possible classifier having to annotate as little data as possible. In order to improve such setting, Knowledge Transfer techniques from another small available anonymisation annotated corpus are explored for seed selection and query selection strategies. Results show that using the proposed seed selection methods obtain the best seeds on which to initialise the base learner's training and that combining source and target classifiers' uncertainties as query strategy improves the Active Learning process, deriving in steeper learning curves and reaching top classifier performance in fewer iterations

A French text-message corpus: 88milSMS. Synthesis and usage

In this article, firstly we briefly summarise the sud4science project and data collection (http://sud4science.org), ensuing processing/analysing stages, and the resulting corpus, 88milSMS (http://88milsms.huma-num.fr), through a synthesis of quotes and references to previous articles (§ 1). Secondly, we provide a state of the art on some research initiatives that use 88milSMS in various domains and frameworks, which will enable future cross-disciplinary insight (§ 2). Then, we present other usages of the 88milSMS corpus we identified through surveys (§ 3). Finally, we suggest future paths for textual data collection and analysis.Dans cet article, nous décrivons synthétiquement le projet sud4science et la collecte de données associée (http://sud4science.org), les étapes de traitement/analyse qui en découlent et le corpus en résultant, 88milSMS (http://88milsms.huma-num.fr). Nous donnons d'abord un aperçu des travaux réalisés dans le cadre de ce projet à travers quelques citations et références (§ 1). Ensuite, nous fournissons un état de l'art sur des initiatives de recherche s'appuyant sur 88milSMS qui s'inscrivent dans des domaines et cadres de travail variés, ce qui ouvre la voie à de nouvelles perspectives interdisciplinaires (§ 2). Puis, nous présentons d'autres usages du corpus 88milSMS que nous avons identifiés via un sondage (§ 3). Enfin, nous faisons quelques propositions pour la collecte et l’analyse de données textuelles