Anne-Lyse Minard


2022

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Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
Alberto Lavelli | Eben Holderness | Antonio Jimeno Yepes | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)

2021

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Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis

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DOING@DEFT : utilisation de lexiques pour une classification efficace de cas cliniques (In this paper, we present our participation to the DEFT 2021 task 1)
Nicolas Hiot | Anne-Lyse Minard | Flora Badin
Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Atelier DÉfi Fouille de Textes (DEFT)

Nous présentons dans cet article notre participation à la tâche 1 de la campagne d’évaluation francophone DEFT 2021, sur l’identification du profil clinique du patient. Nous proposons une méthode évolutive et efficace en temps et en ressources pour la classification de documents médicaux pouvant être facilement adaptée à d’autres domaines de recherche. Notre système a obtenu les meilleures performances sur cette tâche avec une F-mesure de 0,814.

2020

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Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
Graciela Gonzalez-Hernandez | Ari Z. Klein | Ivan Flores | Davy Weissenbacher | Arjun Magge | Karen O'Connor | Abeed Sarker | Anne-Lyse Minard | Elena Tutubalina | Zulfat Miftahutdinov | Ilseyar Alimova
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

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Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020
Ari Klein | Ilseyar Alimova | Ivan Flores | Arjun Magge | Zulfat Miftahutdinov | Anne-Lyse Minard | Karen O’Connor | Abeed Sarker | Elena Tutubalina | Davy Weissenbacher | Graciela Gonzalez-Hernandez
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics. The fifth iteration of the Social Media Mining for Health Applications (#SMM4H) shared tasks sought to advance the use of Twitter data (tweets) for pharmacovigilance, toxicovigilance, and epidemiology of birth defects. In addition to re-runs of three tasks, #SMM4H 2020 included new tasks for detecting adverse effects of medications in French and Russian tweets, characterizing chatter related to prescription medication abuse, and detecting self reports of birth defect pregnancy outcomes. The five tasks required methods for binary classification, multi-class classification, and named entity recognition (NER). With 29 teams and a total of 130 system submissions, participation in the #SMM4H shared tasks continues to grow.

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DOING@DEFT : cascade de CRF pour l’annotation d’entités cliniques imbriquées (DOING@DEFT : cascade of CRF for the annotation of nested clinical entities)
Anne-Lyse Minard | Andréane Roques | Nicolas Hiot | Mirian Halfeld Ferrari Alves | Agata Savary
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Atelier DÉfi Fouille de Textes

Cet article présente le système développé par l’équipe DOING pour la campagne d’évaluation DEFT 2020 portant sur la similarité sémantique et l’extraction d’information fine. L’équipe a participé uniquement à la tâche 3 : “extraction d’information”. Nous avons utilisé une cascade de CRF pour annoter les différentes informations à repérer. Nous nous sommes concentrés sur la question de l’imbrication des entités et de la pertinence d’un type d’entité pour apprendre à reconnaître un autre. Nous avons également testé l’utilisation d’une ressource externe, MedDRA, pour améliorer les performances du système et d’un pipeline plus complexe mais ne gérant pas l’imbrication des entités. Nous avons soumis 3 runs et nous obtenons en moyenne sur toutes les classes des F-mesures de 0,64, 0,65 et 0,61.

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Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

2019

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Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

2018

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KRAUTS: A German Temporally Annotated News Corpus
Jannik Strötgen | Anne-Lyse Minard | Lukas Lange | Manuela Speranza | Bernardo Magnini
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Participation de l’IRISA à DeFT 2018 : classification et annotation d’opinion dans des tweets (IRISA at DeFT 2018: classifying and tagging opinion in tweets )
Anne-Lyse Minard | Christian Raymond | Vincent Claveau
Actes de la Conférence TALN. Volume 2 - Démonstrations, articles des Rencontres Jeunes Chercheurs, ateliers DeFT

Cet article décrit les systèmes développés par l’équipe LinkMedia de l’IRISA pour la campagne d’évaluation DeFT 2018 portant sur l’analyse d’opinion dans des tweets en français. L’équipe a participé à 3 des 4 tâches de la campagne : (i) classification des tweets selon s’ils concernent les transports ou non, (ii) classification des tweets selon leur polarité et (iii) annotation des marqueurs d’opinion et de l’objet à propos duquel est exprimée l’opinion. Nous avons utilisé un algorithme de boosting d’arbres de décision et des réseaux de neurones récurrents (RNN) pour traiter les tâches 1 et 2. Pour la tâche 3 nous avons expérimenté l’utilisation de réseaux de neurones récurrents associés à des CRF. Ces approches donnent des résultats proches, avec un léger avantage aux RNN, et ont permis d’être parmi les premiers classés pour chacune des tâches.

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Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
Alberto Lavelli | Anne-Lyse Minard | Fabio Rinaldi
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

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IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification
Anne-Lyse Minard | Christian Raymond | Vincent Claveau
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task

This paper describes the systems developed by IRISA to participate to the four tasks of the SMM4H 2018 challenge. For these tweet classification tasks, we adopt a common approach based on recurrent neural networks (BiLSTM). Our main contributions are the use of certain features, the use of Bagging in order to deal with unbalanced datasets, and on the automatic selection of difficult examples. These techniques allow us to reach 91.4, 46.5, 47.8, 85.0 as F1-scores for Tasks 1 to 4.

2017

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The Scope and Focus of Negation: A Complete Annotation Framework for Italian
Begoña Altuna | Anne-Lyse Minard | Manuela Speranza
Proceedings of the Workshop Computational Semantics Beyond Events and Roles

In this paper we present a complete framework for the annotation of negation in Italian, which accounts for both negation scope and negation focus, and also for language-specific phenomena such as negative concord. In our view, the annotation of negation complements more comprehensive Natural Language Processing tasks, such as temporal information processing and sentiment analysis. We applied the proposed framework and the guidelines built on top of it to the annotation of written texts, namely news articles and tweets, thus producing annotated data for a total of over 36,000 tokens.

2016

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The Event and Implied Situation Ontology (ESO): Application and Evaluation
Roxane Segers | Marco Rospocher | Piek Vossen | Egoitz Laparra | German Rigau | Anne-Lyse Minard
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents the Event and Implied Situation Ontology (ESO), a manually constructed resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology is built on top of existing resources such as WordNet, SUMO and FrameNet. The ontology is injected to the Predicate Matrix, a resource that integrates predicate and role information from amongst others FrameNet, VerbNet, PropBank, NomBank and WordNet. We illustrate how these resources are used on large document collections to detect information that otherwise would have remained implicit. The ontology is evaluated on two aspects: recall and precision based on a manually annotated corpus and secondly, on the quality of the knowledge inferred by the situation assertions in the ontology. Evaluation results on the quality of the system show that 50% of the events typed and enriched with ESO assertions are correct.

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MEANTIME, the NewsReader Multilingual Event and Time Corpus
Anne-Lyse Minard | Manuela Speranza | Ruben Urizar | Begoña Altuna | Marieke van Erp | Anneleen Schoen | Chantal van Son
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

In this paper, we present the NewsReader MEANTIME corpus, a semantically annotated corpus of Wikinews articles. The corpus consists of 480 news articles, i.e. 120 English news articles and their translations in Spanish, Italian, and Dutch. MEANTIME contains annotations at different levels. The document-level annotation includes markables (e.g. entity mentions, event mentions, time expressions, and numerical expressions), relations between markables (modeling, for example, temporal information and semantic role labeling), and entity and event intra-document coreference. The corpus-level annotation includes entity and event cross-document coreference. Semantic annotation on the English section was performed manually; for the annotation in Italian, Spanish, and (partially) Dutch, a procedure was devised to automatically project the annotations on the English texts onto the translated texts, based on the manual alignment of the annotated elements; this enabled us not only to speed up the annotation process but also provided cross-lingual coreference. The English section of the corpus was extended with timeline annotations for the SemEval 2015 TimeLine shared task. The “First CLIN Dutch Shared Task” at CLIN26 was based on the Dutch section, while the EVALITA 2016 FactA (Event Factuality Annotation) shared task, based on the Italian section, is currently being organized.

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TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks
Bernardo Magnini | Anne-Lyse Minard | Mohammed R. H. Qwaider | Manuela Speranza
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

This paper presents TextPro-AL (Active Learning for Text Processing), a platform where human annotators can efficiently work to produce high quality training data for new domains and new languages exploiting Active Learning methodologies. TextPro-AL is a web-based application integrating four components: a machine learning based NLP pipeline, an annotation editor for task definition and text annotations, an incremental re-training procedure based on active learning selection from a large pool of unannotated data, and a graphical visualization of the learning status of the system.

2015

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SemEval-2015 Task 4: TimeLine: Cross-Document Event Ordering
Anne-Lyse Minard | Manuela Speranza | Eneko Agirre | Itziar Aldabe | Marieke van Erp | Bernardo Magnini | German Rigau | Rubén Urizar
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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HLT-FBK: a Complete Temporal Processing System for QA TempEval
Paramita Mirza | Anne-Lyse Minard
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Proceedings of the First Workshop on Computing News Storylines
Tommaso Caselli | Marieke van Erp | Anne-Lyse Minard | Mark Finlayson | Ben Miller | Jordi Atserias | Alexandra Balahur | Piek Vossen
Proceedings of the First Workshop on Computing News Storylines

2012

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Simplification de phrases pour l’extraction de relations (Sentence Simplification for Relation Extraction) [in French]
Anne-Lyse Minard | Anne-Laure Ligozat | Brigitte Grau
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN

2011

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Apport de la syntaxe pour l’extraction de relations en domaine médical (Contribution of syntax for relation extraction in the medical domain)
Anne-Lyse Minard | Anne-Laure Ligozat | Brigitte Grau
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Dans cet article, nous nous intéressons à l’identification de relations entre entités en domaine de spécialité, et étudions l’apport d’informations syntaxiques. Nous nous plaçons dans le domaine médical, et analysons des relations entre concepts dans des comptes-rendus médicaux, tâche évaluée dans la campagne i2b2 en 2010. Les relations étant exprimées par des formulations très variées en langue, nous avons procédé à l’analyse des phrases en extrayant des traits qui concourent à la reconnaissance de la présence d’une relation et nous avons considéré l’identification des relations comme une tâche de classification multi-classes, chaque catégorie de relation étant considérée comme une classe. Notre système de référence est celui qui a participé à la campagne i2b2, dont la F-mesure est d’environ 0,70. Nous avons évalué l’apport de la syntaxe pour cette tâche, tout d’abord en ajoutant des attributs syntaxiques à notre classifieur, puis en utilisant un apprentissage fondé sur la structure syntaxique des phrases (apprentissage à base de tree kernels) ; cette dernière méthode améliore les résultats de la classification de 3%.

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Extraction d’informations médicales au LIMSI (Medical information extraction at LIMSI)
Cyril Grouin | Louise Deléger | Anne-Lyse Minard | Anne-Laure Ligozat | Asma Ben Abacha | Delphine Bernhard | Bruno Cartoni | Brigitte Grau | Sophie Rosset | Pierre Zweigenbaum
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Démonstrations

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Multi-class SVM for Relation Extraction from Clinical Reports
Anne-Lyse Minard | Anne-Laure Ligozat | Brigitte Grau
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2009

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Corpus Study of Kidney-related Experimental Data in Scientific Papers
Brigitte Grau | Anne-Laure Ligozat | Anne-Lyse Minard
Proceedings of the Workshop on Biomedical Information Extraction