Séminaires du CENTAL (archives 2017-2018)

Organisation 2017-18

Serge Bibauw
Anaïs Tack

 

Calendrier 2017-18

En bref

• 20 octobre 2017 : Natalia Grabar (Université de Lille), Acquisition de ressources pour la simplification de textes médicaux
• 17 novembre 2017 : Naomi Baron (American University), Learning, Knowing, and Remembering in a Digital World
• 24 novembre 2017 : Dirk De Hertog (imec - KU Leuven), Embeddings and their use as features in supervised learning tasks 
• 1^er décembre 2017 : Pierre Deville (Bisnode), Network Science in the era of Text Mining and Big Data
• 8 décembre 2017 : Aline Villavicencio (University of Essex • Federal University of Rio Grande do Sul), Identifying Idiomatic Language with Distributional Semantic Models

 

• 9 mars 2018 : Leonie Grön (KU Leuven), Term variation in clinical records: First insights from a corpus study
• 30 mars 2018 : Eric Kergosien (Université de Lille), Analyse spatiale des médias numériques via des approches de fouille de textes
• 20 avril 2018 : Henning Wachsmuth (University of Paderborn), Computational Assessment of Argumentation Quality
• 4 mai 2018 : Pierre Lison (Norwegian Computing Center), Modélisation du dialogue: systèmes de dialogue parlé et corpus multilingues
• 18 mai 2018 : PLIN Linguistic Day 2018, Technological innovation in language learning and teaching

 

Programme complet

 

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Natalia Grabar (STL, Université de Lille, CNRS, FR)

Acquisition de ressources pour la simplification de textes médicaux

Une des particularités des textes médicaux consiste en utilisation de termes techniques très spécialisés, qui restent souvent non compréhensibles pour les locuteurs. Lors de la simplification de ces textes, il est donc important de disposer de ressources nécessaires. Nous introduisons ici deux méthodes pour effectuer l'acquisition de telles ressources. L'une repose sur les indices internes des termes (l'analyse morphologique des termes composés) alors que l'autre exploite les indices externes des termes (les reformulations effectuées dans les textes). Aucune de ces méthodes ne requiert l'exploitation de corpus parallèles. Nous décrivons et discutons les résultats.

mots-clés : simplification de textes · acquisition de ressources · définition de règles · catégorisation · domaine médical

références : 

• Antoine, E., & Grabar, N. (2016). Exploitation de reformulations pour l’acquisition d’un vocabulaire expert/non expert. In Actes de la conférence conjointe JEP-TALN-RECITAL 2016, volume 2 : TALN (pp. 153–166).
• Grabar, N., & Hamon, T. (2016). A large rated lexicon with French medical words. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) (pp. 2643–48).
• Grabar, N., & Hamon, T. (2016). Exploitation de la morphologie pour l’extraction automatique de paraphrases grand public des termes médicaux. TAL, 57(1), 85–109.

diapositives : ici

 

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Vendredi 17 novembre 2017, 14h-15h, Auditoire MONTESQUIEU 3
Séminaire co-organisé avec l'Institut Langage et Communication (IL&C)

Naomi Baron (CTRL, American University, Washington, D.C., US)

Learning, Knowing, and Remembering in a Digital World

mots-clés : cognition · digital impact on memory · educational curricula · GPS

Digital tools such as the internet, search engines, and online navigation have put a wealth of information at our fingertips. Are these same tools impacting the way we use human cognitive skills to learn, know, and remember? Research suggests that availability of “google knowing” is redefining our assumptions about what kinds of data – and knowledge – are appropriately held in our own heads. These redefinitions are, in turn, reshaping academic curricula, for good or for ill.

références : ici

diffusion en direct et vidéo : www.facebook.com/didaxoUlearn/videos/1174447416020956

diapositives : ici

 

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Vendredi 24 novembre 2017, 14h-15h, Collège Erasme c.142

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Vendredi 1^er décembre 2017, 14h-15h, Collège Erasme c.142

Network Science in the era of Text Mining and Big Data

mots-clés : networks · big data · visualization

Networks are everywhere. From micro to macro, they pervade our world. Understanding their structure and behaviour has been a major concern recently. However, while tremendous progress has been made on natural networks, we still scramble when it comes to human or business behavior. But this is about to change, as we are now entering the data driven age. Former PhD student at UCL, young entrepreneur and professor at Solvay, Pierre will discuss the opportunities offered by the big data revolution and text mining tools and how it relates to his research on network analysis.

références : 

• Deville, P., Wang, D., Sinatra, R., Song, C., Blondel, V. D., & Barabási, A.-L. (2014). Career on the Move: Geography, Stratification, and Scientific Impact. Scientific Reports, 4, srep04770. https://doi.org/10.1038/srep04770
• Sinatra, R., Deville, P., Szell, M., Wang, D., & Barabási, A.-L. (2015). A century of physics. Nature Physics, 11(10), 791–796. https://doi.org/10.1038/nphys3494
• Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312). https://doi.org/10.1126/science.aaf5239

 

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Vendredi 8 décembre 2017, 14h-15h, Collège Erasme c.142

Aline Villavicencio (University of Essex, UK • INF, Federal University of Rio Grande do Sul, BR)

Identifying Idiomatic Language with Distributional Semantic Models

Precise natural language understanding requires adequate treatments both of single words and of larger units. However, expressions like compound nouns may display idiomaticity, and while a police car is a car used by the police, a loan shark is not a fish that can be borrowed. Therefore it is important to identify which expressions are idiomatic, and which are not, as the latter can be interpreted from a combination of the meanings of their component words while the former cannot. In this talk I discuss the ability of distributional semantic models (DSMs) to capture idiomaticity in compounds, by means of a large-scale multilingual evaluation of DSMs in French and English. A total of 816 DSMs were constructed in 2,856 evaluations. The results obtained show a high correlation with human judgments about compound idiomaticity  (Spearman’s ρ=.82 in one dataset), indicating that these models are able to successfully detect idiomaticity.

mots-clés : idiomaticity · distributional semantic models · compound nouns · multiword expressions

références : 

• Wilkens, R., Zilio, L., Cordeiro, S. R., Ramisch, C., Idiart, M., & Villavicencio, A. (2017). LexSubNC: A Dataset of Lexical Substitution for Nominal Compounds. In Proceedings of the 12th International Conference on Computational Semantics (IWCS). Montpellier.
• Cordeiro, S., Ramisch, C., Idiart, M., & Villavicencio, A. (2016). Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers, pp. 1986–1997). Berlin, Germany: Association for Computational Linguistics. https://doi.org/10.18653/v1/P16-1187
• Ramisch, C., Cordeiro, S., Zilio, L., Idiart, M., & Villavicencio, A. (2016). How Naked is the Naked Truth? A Multilingual Lexicon of Nominal Compound Compositionality. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016) (Vol. 2: Short Papers, pp. 156–161). Berlin, Germany: ACL. http://aclweb.org/anthology/P/P16/P16-2026.pdf
• Cordeiro, S., Ramisch, C., & Villavicencio, A. (2016). mwetoolkit+sem: Integrating Word Embeddings in the mwetoolkit for Semantic MWE Processing. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) (pp. 1221–1225). Portorož, Slovenia: European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2016/summaries/271.html

 

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Leonie Grön (QLVL, KU Leuven, BE)

Term variation in clinical records: First insights from a corpus study

In clinical documentation, we encounter a range of term variants that goes far beyond the standard forms found in medical ontologies. For instance, in Dutch, the concept of high blood pressure can be expressed by a vernacular expression (bloedhoogdruk ‘high blood pressure’) or a specialized term (hypertensie ‘hypertension’); in addition, we find regular morphological alternations (‘verhoogde bloeddruk ‘elevated blood pressure’, hypertens ‘hypertensive’), abbreviations (bhd, ht) and numerous idiosyncratic variants.

Medical term variation poses a challenge to the automatic processing of clinical documents, such as the automatic assignment of ontological codes to electronic health records (EHRs): As medical ontologies do not cover non-standard variants, knowledge-based methods to named entity recognition (NER) are prone to miss a considerable portion of relevant entities in text. On the other hand, machine learning and distributional approaches struggle with the high proportion of idiosyncracies, as well as the stylistic features of EHRs, which are typically composed in a non-grammatical, telegraphic style. To improve NER applications for the clinical domain, it is thus crucial to, firstly, identify patterns of variation between term types and, secondly, detect the context factors that motivate such alternations. For instance, the individual sections within an EHR (e.g. physical examination vs. clinical conclusion) may show distinctive proportions of terms from certain semantic categories (e.g. procedures of examination vs. diagnoses) and terms from a particular register (e.g. vernacular vs. specialized). Such correlations can be utilized to compose a domain-specific feature set for clinical NER.

To assess term variation in clinical Dutch, we conducted a corpus study based on a sample of EHRs from endocrinology. Altogether, the medical histories of 180 patients with diabetes were labelled with codes from the clinical terminology SNOMED-CT. After reporting on methodological challenges encountered during the annotation process, I will present preliminary results from the analysis of the annotated data. Starting from a typology of variation types, I will assess the overlap with the terms covered by standard medical ontologies. Then, I will investigate the term distribution in our corpus based on the relative frequency of term types across the individual case histories, and the different EHR sections. To conclude, I will summarize my initial findings on the influence of context factors, as well as semantic properties of the underlying concept, on term variation.

mots-clés : terminological variation · clinical sublanguage · electronic health records

références : 

• Grön  L., Bertels  A. (2018). Clinical  sublanguages:  Vocabulary  structure  and  its  impact  on  term  weighting. Terminology, 24 (1).

diapositives : ici