Large language models (LLMs) have become the preferred solution for many natural language processing tasks. In low-resource environments such as specialized domains, their few-shot capabilities are expected to deliver high performance. Named Entity Recognition (NER) is a critical task in information extraction that is not covered in recent LLM benchmarks. There is a need for better understanding the performance of LLMs for NER in a variety of settings including languages other than English. This study aims to evaluate generative LLMs, employed through prompt engineering, for few-shot clinical NER. We compare 13 auto-regressive models using prompting and 16 masked models using fine-tuning on 14 NER datasets covering English, French and Spanish. While prompt-based auto-regressive models achieve competitive F1 for general NER, they are outperformed within the clinical domain by lighter biLSTM-CRF taggers based on masked models. Additionally, masked models exhibit lower environmental impact compared to auto-regressive models. Findings are consistent across the three languages studied, which suggests that LLM prompting is not yet suited for NER production in the clinical domain.

Les grands modèles de langage deviennent la solution de choix pour de nombreuses tâches de traitement du langage naturel, y compris dans des domaines spécialisés où leurs capacités few-shot devraient permettre d’obtenir des performances élevées dans des environnements à faibles ressources. Cependant, notre évaluation de 10 modèles auto-régressifs et 16 modèles masqués montre que, bien que les modèles auto-régressifs utilisant des prompts puissent rivaliser en termes de reconnaissance d’entités nommées (REN) en dehors du domaine clinique, ils sont dépassés dans le domaine clinique par des taggers biLSTM-CRF plus légers reposant sur des modèles masqués. De plus, les modèles masqués ont un bien moindre impact environnemental que les modèles auto-régressifs. Ces résultats, cohérents dans les trois langues étudiées, suggèrent que les modèles à apprentissage few-shot ne sont pas encore adaptés à la production de REN dans le domaine clinique, mais pourraient être utilisés pour accélérer la création de données annotées de qualité.

Ce papier explore deux approches pour répondre aux questions à choix multiples (QCM) de pharmacie du défi DEFT 2024 en utilisant des modèles de langue (LLMs) entraînés sur des données ouvertes avec moins de 3 milliards de paramètres. Les deux approches reposent sur l’architecture RAG (Retrieval Augmented Generation) pour combiner la récupération de contexte à partir de bases de connaissances externes (NACHOS et Wikipédia) avec la génération de réponses par le LLM Apollo-2B. La première approche traite directement les QCMs et génère les réponses en une seule étape, tandis que la seconde approche reformule les QCMs en questions binaires (Oui/Non) puis génère une réponse pour chaque question binaire. Cette dernière approche obtient un Exact Match Ratio de 14.7 et un Hamming Score de 51.6 sur le jeu de test, ce qui démontre le potentiel du RAG pour des tâches de Q/A sous de telles contraintes.

Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora while remaining lighter than modern Large Language Models (MLMs). Recently, several MLMs have been released for the biomedical domain in French, and experiments suggest that they outperform standard French counterparts. However, no systematic evaluation comparing all models on the same corpora is available. Objective: This paper presents an evaluation of masked language models for biomedical French on the task of clinical named entity recognition. Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them to standard French models CamemBERT, FlauBERT and FrAlBERT as well as multilingual mBERT using three publically available corpora for clinical named entity recognition in French. The evaluation set-up relies on gold-standard corpora as released by the corpus developers. Results: Results suggest that CamemBERT-bio outperforms DrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbon footprint. Conclusion: This is the first benchmark evaluation of biomedical masked language models for French clinical entity recognition that compares model performance consistently on nested entity recognition using metrics covering performance and environmental impact.

We explore the task of event extraction and classification by harnessing the power of distant supervision. We present a novel text labeling method that leverages the redundancy of temporal information in a data lake. This method enables the creation of a large programmatically annotated corpus, allowing the training of transformer models using distant supervision. This aims to reduce expert annotation time, a scarce and expensive resource. Our approach utilizes temporal redundancy between structured sources and text, enabling the design of a replicable framework applicable to diverse real-world databases and use cases. We employ this method to create multiple silver datasets to reconstruct key events in cancer patients’ pathways, using clinical notes from a cohort of 380,000 oncological patients. By employing various noise label management techniques, we validate our end-to-end approach and compare it with a baseline classifier built on expert-annotated data. The implications of our work extend to accelerating downstream applications, such as patient recruitment for clinical trials, treatment effectiveness studies, survival analysis, and epidemiology research. While our study showcases the potential of the method, there remain avenues for further exploration, including advanced noise management techniques, semi-supervised approaches, and a deeper understanding of biases in the generated datasets and models.

We consider the task of automatically extracting various overlapping frames, i.e, structured entities composed of multiple labels and mentions, from long clinical breast radiology documents. While many methods exist for related topics such as event extraction, slot filling, or discontinuous entity recognition, a challenge in our study resides in the fact that clinical reports typically contain overlapping frames that span multiple sentences or paragraphs. We propose a new method that addresses these difficulties and evaluate it on a new annotated corpus. Despite the small number of documents, we show that the hybridization between knowledge injection and a learning-based system allows us to quickly obtain proper results. We will also introduce the concept of scope relations and show that it both improves the performance of our system, and provides a visual explanation of the predictions.

Extracting temporal relations usually entails identifying and classifying the relation between two mentions. However, the definition of temporal mentions strongly depends on the text type and the application domain. Clinical text in particular is complex. It may describe events that occurred at different times, contain redundant information and a variety of domain-specific temporal expressions. In this paper, we propose a novel event-independent representation of temporal relations that is task-independent and, therefore, domain-independent. We are interested in identifying homogeneous text portions from a temporal standpoint and classifying the relation between each text portion and the document creation time. Temporal relation extraction is cast as a sequence labeling task and evaluated on oncology notes. We further evaluate our temporal representation by the temporal positioning of toxicity events of chemotherapy administrated to colon and lung cancer patients described in French clinical reports. An overall macro F-measure of 0.86 is obtained for temporal relation extraction by a neural token classification model trained on clinical texts written in French. Our results suggest that the toxicity event extraction task can be performed successfully by automatically identifying toxicity events and placing them within the patient timeline (F-measure .62). The proposed system has the potential to assist clinicians in the preparation of tumor board meetings.

Although recent years have been marked by incredible advances in the whole development process of NLP systems, there are still blind spots in characterizing what is still hampering real-world adoption of models in knowledge-intensive settings. In this paper, we illustrate through a real-world zero-shot text search case for information seeking in scientific papers, the masked phenomena that the current process of measuring performance might not reflect, even when benchmarks are, in appearance, faithfully representative of the task at hand. In addition to experimenting with TREC-COVID and NFCorpus, we provide an industrial, expert-carried/annotated, case of studying vitamin B’s impact on health. We thus discuss the misalignment between solely focusing on single-metric performance as a criterion for model choice and relevancy as a subjective measure for meeting a user’s need.

L’annotation manuelle de corpus est un processus coûteux et lent, notamment pour la tâche de re-connaissance d’entités nommées. L’apprentissage actif vise à rendre ce processus plus efficace, ensélectionnant les portions les plus pertinentes à annoter. Certaines stratégies visent à sélectionner lesportions les plus représentatives du corpus, d’autres, les plus informatives au modèle de langage.Malgré un intérêt grandissant pour l’apprentissage actif, rares sont les études qui comparent cesdifférentes stratégies dans un contexte de reconnaissance d’entités nommées médicales. Nous pro-posons une comparaison de ces stratégies en fonction des performances de chacune sur 3 corpus dedocuments cliniques en langue française : MERLOT, QuaeroFrenchMed et E3C. Nous comparonsles stratégies de sélection mais aussi les différentes façons de les évaluer. Enfin, nous identifions lesstratégies qui semblent les plus efficaces et mesurons l’amélioration qu’elles présentent, à différentesphases de l’apprentissage.

L’extraction de relations temporelles consiste à identifier et classifier la relation entre deux mentions. Néanmoins, la définition des mentions temporelles dépend largement du type du texte et du domained’application. En particulier, le texte clinique est complexe car il décrit des évènements se produisant à des moments différents et contient des informations redondantes et diverses expressions temporellesspécifiques au domaine. Dans cet article, nous proposons une nouvelle représentation des relations temporelles, qui est indépendante du domaine et de l’objectif de la tâche d’extraction. Nous nousintéressons à extraire la relation entre chaque portion du texte et la date de création du document. Nous formulons l’extraction de relations temporelles comme une tâche d’étiquetage de séquences.Une macro F-mesure de 0,8 est obtenue par un modèle neuronal entraîné sur des textes cliniques, écrits en français. Nous évaluons notre représentation temporelle par le positionnement temporel desévènements de toxicité des chimiothérapies.

There are still hurdles standing in the way of faster and more efficient knowledge consumption in industrial environments seeking to foster innovation. In this work, we address the portability of extractive Question Answering systems from academic spheres to industries basing their decisions on thorough scientific papers analysis. Keeping in mind that such industrial contexts often lack high-quality data to develop their own QA systems, we illustrate the misalignment between application requirements and cost sensitivity of such industries and some widespread practices tackling the domain-adaptation problem in the academic world. Through a series of extractive QA experiments on QASPER, we adopt the pipeline-based retriever-ranker-reader architecture for answering a question on a scientific paper and show the impact of modeling choices in different stages on the quality of answer prediction. We thus provide a characterization of practical aspects of real-life application scenarios and notice that appropriate trade-offs can be efficient and add value in those industrial environments.

La première tâche du Défi fouille de textes 2021 a consisté à extraire automatiquement, à partir de cas cliniques, les phénotypes pathologiques des patients regroupés par tête de chapitre du MeSH-maladie. La solution présentée est celle d’un classifieur multilabel basé sur un transformer. Deux transformers ont été utilisés : le camembert-large classique (run 1) et le camembert-large fine-tuné (run 2) sur des articles biomédicaux français en accès libre. Nous avons également proposé un modèle « bout-enbout », avec une première phase d’extraction d’entités nommées également basée sur un transformer de type camembert-large et un classifieur de genre sur un modèle Adaboost. Nous obtenons un très bon rappel et une précision correcte, pour une F1-mesure autour de 0,77 pour les trois runs. La performance du modèle « bout-en-bout » est similaire aux autres méthodes.

La résolution de la coréférence est un élément essentiel pour la constitution automatique de chronologies médicales à partir des dossiers médicaux électroniques. Dans ce travail, nous présentons une approche neuronale pour la résolution de la coréférence dans des textes médicaux écrits en anglais pour les entités générales et cliniques en nous évaluant dans le cadre de référence pour cette tâche que constitue la tâche 1C de la campagne i2b2 2011.

Nous présentons dans cet article les méthodes conçues et les résultats obtenus lors de notre participation à la tâche 3 de la campagne d’évaluation DEFT 2020, consistant en la reconnaissance d’entités nommées du domaine médical. Nous proposons deux modèles différents permettant de prendre en compte les entités imbriquées, qui représentent une des difficultés du jeu de données proposées, et présentons les résultats obtenus. Notre meilleur run obtient la meilleure performance parmi les participants, sur l’une des deux sous-tâches du défi.

Nous présentons dans cet article les méthodes conçues et les résultats obtenus lors de notre participation à la tâche 3 de la campagne d’évaluation DEFT 2019. Nous avons utilisé des approches simples à base de règles ou d’apprentissage automatique, et si nos résultats sont très bons sur les informations simples à extraire comme l’âge et le sexe du patient, ils restent mitigés sur les tâches plus difficiles.

Many applications in biomedical natural language processing rely on sequence tagging as an initial step to perform more complex analysis. To support text analysis in the biomedical domain, we introduce Yet Another SEquence Tagger (YASET), an open-source multi purpose sequence tagger that implements state-of-the-art deep learning algorithms for sequence tagging. Herein, we evaluate YASET on part-of-speech tagging and named entity recognition in a variety of text genres including articles from the biomedical literature in English and clinical narratives in French. To further characterize performance, we report distributions over 30 runs and different sizes of training datasets. YASET provides state-of-the-art performance on the CoNLL 2003 NER dataset (F1=0.87), MEDPOST corpus (F1=0.97), MERLoT corpus (F1=0.99) and NCBI disease corpus (F1=0.81). We believe that YASET is a versatile and efficient tool that can be used for sequence tagging in biomedical and clinical texts.

In this paper we present our participation to SemEval 2017 Task 12. We used a neural network based approach for entity and temporal relation extraction, and experimented with two domain adaptation strategies. We achieved competitive performance for both tasks.

In this paper, we present a method for temporal relation extraction from clinical narratives in French and in English. We experiment on two comparable corpora, the MERLOT corpus and the THYME corpus, and show that a common approach can be used for both languages.

In this paper, we present an unsupervised pipeline approach for clustering news articles based on identified event instances in their content. We leverage press agency newswire and monolingual word alignment techniques to build meaningful and linguistically varied clusters of articles from the web in the perspective of a broader event type detection task. We validate our approach on a manually annotated corpus of Web articles.

We present a neural architecture for containment relation identification between medical events and/or temporal expressions. We experiment on a corpus of de-identified clinical notes in English from the Mayo Clinic, namely the THYME corpus. Our model achieves an F-measure of 0.613 and outperforms the best result reported on this corpus to date.

La désambiguïsation d’entités (ou liaison d’entités), qui consiste à relier des mentions d’entités d’un texte à des entités d’une base de connaissance, est un problème qui se pose, entre autre, pour le peuplement automatique de bases de connaissances à partir de textes. Une difficulté de cette tâche est la résolution d’ambiguïtés car les systèmes ont à choisir parmi un nombre important de candidats. Cet article propose une nouvelle approche fondée sur l’apprentissage joint de représentations distribuées des mots et des entités dans le même espace, ce qui permet d’établir un modèle robuste pour la comparaison entre le contexte local de la mention d’entité et les entités candidates.

L’analyse temporelle des documents cliniques permet d’obtenir des représentations riches des informations contenues dans les dossiers électroniques patient. Cette analyse repose sur l’extraction d’événements, d’expressions temporelles et des relations entre eux. Dans ce travail, nous considérons que nous disposons des événements et des expressions temporelles pertinents et nous nous intéressons aux relations temporelles entre deux événements ou entre un événement et une expression temporelle. Nous présentons des modèles de classification supervisée pour l’extraction de des relations en français et en anglais. Les performances obtenues sont comparables dans les deux langues, suggérant ainsi que différents domaines cliniques et différentes langues pourraient être abordés de manière similaire.

Aspect Based Sentiment Analysis (ABSA) is the task of mining and summarizing opinions from text about specific entities and their aspects. This article describes two datasets for the development and testing of ABSA systems for French which comprise user reviews annotated with relevant entities, aspects and polarity values. The first dataset contains 457 restaurant reviews (2365 sentences) for training and testing ABSA systems, while the second contains 162 museum reviews (655 sentences) dedicated to out-of-domain evaluation. Both datasets were built as part of SemEval-2016 Task 5 “Aspect-Based Sentiment Analysis” where seven different languages were represented, and are publicly available for research purposes.

This article presents a corpus for development and testing of event schema induction systems in English. Schema induction is the task of learning templates with no supervision from unlabeled texts, and to group together entities corresponding to the same role in a template. Most of the previous work on this subject relies on the MUC-4 corpus. We describe the limits of using this corpus (size, non-representativeness, similarity of roles across templates) and propose a new, partially-annotated corpus in English which remedies some of these shortcomings. We make use of Wikinews to select the data inside the category Laws & Justice, and query Google search engine to retrieve different documents on the same events. Only Wikinews documents are manually annotated and can be used for evaluation, while the others can be used for unsupervised learning. We detail the methodology used for building the corpus and evaluate some existing systems on this new data.

Les références à des phénomènes du monde réel et à leur caractérisation temporelle se retrouvent dans beaucoup de types de discours en langue naturelle. Ainsi, l’analyse temporelle apparaît comme un élément important en traitement automatique de la langue. Cet article présente une analyse de textes en domaine de spécialité du point de vue temporel. En s’appuyant sur un corpus de documents issus de plusieurs dossiers électroniques patient désidentifiés, nous décrivons la construction d’une ressource annotée en expressions temporelles selon la norme TimeML. Par suite, nous utilisons cette ressource pour évaluer plusieurs méthodes d’extraction automatique d’expressions temporelles adaptées au domaine médical. Notre meilleur système statistique offre une performance de 0,91 de F-mesure, surpassant pour l’identification le système état de l’art HeidelTime. La comparaison de notre corpus de travail avec le corpus journalistique FR-Timebank permet également de caractériser les différences d’utilisation des expressions temporelles dans deux domaines de spécialité.

Cet article présente un modèle génératif pour l’induction non supervisée d’événements. Les précédentes méthodes de la littérature utilisent uniquement les têtes des syntagmes pour représenter les entités. Pourtant, le groupe complet (par exemple, ”un homme armé”) apporte une information plus discriminante (que ”homme”). Notre modèle tient compte de cette information et la représente dans la distribution des schémas d’événements. Nous montrons que ces relations jouent un rôle important dans l’estimation des paramètres, et qu’elles conduisent à des distributions plus cohérentes et plus discriminantes. Les résultats expérimentaux sur le corpus de MUC-4 confirment ces progrès.

Web pages do not offer reliable metadata concerning their creation date and time. However, getting the document creation time is a necessary step for allowing to apply temporal normalization systems to web pages. In this paper, we present DCTFinder, a system that parses a web page and extracts from its content the title and the creation date of this web page. DCTFinder combines heuristic title detection, supervised learning with Conditional Random Fields (CRFs) for document date extraction, and rule-based creation time recognition. Using such a system allows further deep and efficient temporal analysis of web pages. Evaluation on three corpora of English and French web pages indicates that the tool can extract document creation times with reasonably high accuracy (between 87 and 92%). DCTFinder is made freely available on http://sourceforge.net/projects/dctfinder/, as well as all resources (vocabulary and annotated documents) built for training and evaluating the system in English and French, and the English trained model itself.

In this paper, we describe the development of French resources for the extraction and normalization of temporal expressions with HeidelTime, a open-source multilingual, cross-domain temporal tagger. HeidelTime extracts temporal expressions from documents and normalizes them according to the TIMEX3 annotation standard. Several types of temporal expressions are extracted: dates, times, durations and temporal sets. French resources have been evaluated in two different ways: on the French TimeBank corpus, a corpus of newspaper articles in French annotated according to the ISO-TimeML standard, and on a user application for automatic building of event timelines. Results on the French TimeBank are quite satisfaying as they are comparable to those obtained by HeidelTime in English and Spanish on newswire articles. Concerning the user application, we used two temporal taggers for the preprocessing of the corpus in order to compare their performance and results show that the performances of our application on French documents are better with HeidelTime. The French resources and evaluation scripts are publicly available with HeidelTime.

This article introduces a novel protocol and resource to evaluate Web-as-corpus topical document retrieval. To the contrary of previous work, our goal is to provide an automatic, reproducible and robust evaluation for this task. We rely on the OpenDirectory (DMOZ) as a source of topically annotated webpages and index them in a search engine. With this OpenDirectory search engine, we can then easily evaluate the impact of various parameters such as the number of seed terms, queries or documents, or the usefulness of various term selection algorithms. A first fully automatic evaluation is described and provides baseline performances for this task. The article concludes with practical information regarding the availability of the index and resource files.

We present a new measure of thematic cohesion. This measure associates each term with a weight representing its discriminatory power toward a theme, this theme being itself expressed by a list of terms (a thematic lexicon). This thematic cohesion criterion can be used in many applications, such as query expansion, computer-assisted translation, or iterative construction of domain-specific lexicons and corpora. The measure is computed in two steps. First, a set of documents related to the terms is gathered from the Web by querying a Web search engine. Then, we produce an oriented co-occurrence graph, where vertices are the terms and edges represent the fact that two terms co-occur in a document. This graph can be interpreted as a recommendation graph, where two terms occurring in a same document means that they recommend each other. This leads to using a random walk algorithm that assigns a global importance value to each vertex of the graph. After observing the impact of various parameters on those importance values, we evaluate their correlation with retrieval effectiveness.

This article presents WebAnnotator, a new tool for annotating Web pages. WebAnnotator is implemented as a Firefox extension, allowing annotation of both offline and inline pages. The HTML rendering fully preserved and all annotations consist in new HTML spans with specific styles. WebAnnotator provides an easy and general-purpose framework and is made available under CeCILL free license (close to GNU GPL), so that use and further contributions are made simple. All parts of an HTML document can be annotated: text, images, videos, tables, menus, etc. The annotations are created by simply selecting a part of the document and clicking on the relevant type and subtypes. The annotated elements are then highlighted in a specific color. Annotation schemas can be defined by the user by creating a simple DTD representing the types and subtypes that must be highlighted. Finally, annotations can be saved (HTML with highlighted parts of documents) or exported (in a machine-readable format).

Within the general purpose of information extraction, detection of event descriptions is an important clue. A word refering to an event is more powerful than a single word, because it implies a location, a time, protagonists (persons, organizations\dots). However, if verbal designations of events are well studied and easier to detect than nominal ones, nominal designations do not claim as much definition effort and resources. In this work, we focus on nominals desribing events. As our application domain is information extraction, we follow a named entity approach to describe and annotate events. In this paper, we present a typology and annotation guidelines for event nominals annotation. We applied them to French newswire articles and produced an annotated corpus. We present observations about the designations used in our manually annotated corpus and the behavior of their triggers. We provide statistics concerning word ambiguity and context of use of event nominals, as well as machine learning experiments showing the difficulty of using lexicons for extracting events.

This article presents work carried out within the framework of the ongoing ANR (French National Research Agency) project Chronolines, which focuses on the temporal processing of large news-wire corpora in English and French. The aim of the project is to create new and innovative interfaces for visualizing textual content according to temporal criteria. Extracting and normalizing the temporal information in texts through linguistic annotation is an essential step towards attaining this objective. With this goal in mind, we developed a set of guidelines for the annotation of temporal and event expressions that is intended to be compatible with the TimeML markup language, while addressing some of its pitfalls. We provide results of an initial application of these guidelines to real news-wire texts in French over several iterations of the annotation process. These results include inter-annotator agreement figures and an error analysis. Our final inter-annotator agreement figures compare favorably with those reported for the TimeBank 1.2 annotation project.

Within the general purpose of information extraction, detection of event descriptions is often an important clue. An important characteristic of event designation in texts, and especially in media, is that it changes over time. Understanding how these designations evolve is important in information retrieval and information extraction. Our first hypothesis is that, when an event first occurs, media relate it in a very descriptive way (using verbal designations) whereas after some time, they use shorter nominal designations instead. Our second hypothesis is that the number of different nominal designations for an event tends to stabilize itself over time. In this article, we present our methodology concerning the study of the evolution of event designations in French documents from the news agency AFP. For this preliminary study, we focused on 7 topics which have been relatively important in France. Verbal and nominal designations of events have been manually annotated in manually selected topic-related passages. This French corpus contains a total of 2064 annotations. We then provide preliminary interesting statistical results and observations concerning these evolutions.

In this paper, we present the founding elements of a formal model of the evaluation paradigm in natural language processing. We propose an abstract model of objective quantitative evaluation based on rough sets, as well as the notion of potential performance space for describing the performance variations corresponding to the ambiguity present in hypothesis data produced by a computer program, when comparing it to the reference data created by humans. A formal model of the evaluation paradigm will be useful for comparing evaluations protocols, investigating evaluation constraint relaxation and getting a better understanding of the evaluation paradigm, provided it is general enough to be able to represent any natural language processing task.

Nous présentons dans cet article un générateur automatique de questions pour le français. Le système de génération procède par transformation de phrases déclaratives en interrogatives et se base sur une analyse syntaxique préalable de la phrase de base. Nous détaillons les différents types de questions générées. Nous présentons également une évaluation de l’outil, qui démontre que 41 % des questions générées par le système sont parfaitement bien formées.

Cet article décrit une étude sur l’annotation automatique des noms d’événements dans les textes en français. Plusieurs lexiques existants sont utilisés, ainsi que des règles syntaxiques d’extraction, et un lexique composé de façon automatique, permettant de fournir une valeur sur le niveau d’ambiguïté du mot en tant qu’événement. Cette nouvelle information permettrait d’aider à la désambiguïsation des noms d’événements en contexte.

La plupart des systèmes de question-réponse ont été conçus pour répondre à des questions dites “factuelles” (réponses précises comme des dates, des lieux), et peu se sont intéressés au traitement des questions complexes. Cet article présente une typologie des questions en y incluant les questions complexes, ainsi qu’une typologie des formes de réponses attendues pour chaque type de questions. Nous présentons également des expériences préliminaires utilisant ces typologies pour les questions complexes, avec de bons résultats.

L’extraction des événements désignés par des noms est peu étudiée dans des corpus généralistes. Si des lexiques de noms déclencheurs d’événements existent, les problèmes de polysémie sont nombreux et beaucoup d’événements ne sont pas introduits par des déclencheurs. Nous nous intéressons dans cet article à une hypothèse selon laquelle les verbes induisant la cause ou la conséquence sont de bons indices quant à la présence d’événements nominaux dans leur cotexte.

This paper presents the participation of FIDJI system to the Web Question-Answering evaluation campaign organized by Quaero in 2009. FIDJI is an open-domain question-answering system which combines syntactic information with traditional QA techniques such as named entity recognition and term weighting in order to validate answers through multiple documents. It was originally designed to process ``clean'' document collections. Overall results are significantly lower than in traditional campaigns but results (for French evaluation) are quite good compared to other state-of-the-art systems. They show that a syntax-based strategy, applied on uncleaned Web data, can still obtain good results. Moreover, we obtain much higher scores on ``complex'' questions, i.e. `how' and `why' questions, which are more representative of real user needs. These results show that questioning the Web with advanced linguistic techniques can be done without heavy pre-processing and with results that come near to best systems that use strong resources and large structured indexes.

The Quaero project organized a set of evaluations of Named Entity recognition systems in 2009. One of the sub-tasks consists in extracting citations from patents, i.e. references to other documents, either other patents or general literature from English-language patents. We present in this paper the participation of LIMSI in this evaluation, with a complete system description and the evaluation results. The corpus shown that patent and non-patent citations have a very different nature. We then separated references to other patents and to general literature papers and we created a hybrid system. For patent citations, the system used rule-based expert knowledge on the form of regular expressions. The system for detecting non-patent citations, on the other hand, is purely stochastic (machine learning with CRF++). Then we mixed both approaches to provide a single output. 4 teams participated to this task and our system obtained the best results of this evaluation campaign, even if the difference between the first two systems is poorly significant.

In the QA and information retrieval domains progress has been assessed via evaluation campaigns(Clef, Ntcir, Equer, Trec).In these evaluations, the systems handle independent questions and should provide one answer to each question, extracted from textual data, for both open domain and restricted domain. Quæro is a program promoting research and industrial innovation on technologies for automatic analysis and classification of multimedia and multilingual documents. Among the many research areas concerned by Quæro. The Quaero project organized a series of evaluations of Question Answering on Web Data systems in 2008 and 2009. For each language, English and French the full corpus has a size of around 20Gb for 2.5M documents. We describe the task and corpora, and especially the methodologies used in 2008 to construct the test of question and a new one in the 2009 campaign. Six types of questions were addressed, factual, Non-factual(How, Why, What), List, Boolean. A description of the participating systems and the obtained results is provided. We show the difficulty for a question-answering system to work with complex data and questions.

The Quæro program that promotes research and industrial innovation on technologies for automatic analysis and classification of multimedia and multilingual documents. Within its context a set of evaluations of Named Entity recognition systems was held in 2009. Four tasks were defined. The first two concerned traditional named entities in French broadcast news for one (a rerun of ESTER 2) and of OCR-ed old newspapers for the other. The third was a gene and protein name extraction in medical abstracts. The last one was the detection of references in patents. Four different partners participated, giving a total of 16 systems. We provide a synthetic descriptions of all of them classifying them by the main approaches chosen (resource-based, rules-based or statistical), without forgetting the fact that any modern system is at some point hybrid. The metric (the relatively standard Slot Error Rate) and the results are also presented and discussed. Finally, a process is ongoing with preliminary acceptance of the partners to ensure the availability for the community of all the corpora used with the exception of the non-Quæro produced ESTER 2 one.

Question answering (QA) systems aim at retrieving precise information from a large collection of documents. To be considered as reliable by users, a QA system must provide elements to evaluate the answer. This notion of answer justification can also be useful when developping a QA system in order to give criteria for selecting correct answers. An answer justification can be found in a sentence, a passage made of several consecutive sentences or several passages of a document or several documents. Thus, we are interesting in pinpointing the set of information that allows to verify the correctness of the answer in a candidate passage and the question elements that are missing in this passage. Moreover, the relevant information is often given in texts in a different form from the question form: anaphora, paraphrases, synonyms. In order to have a better idea of the importance of all the phenomena we underlined, and to provide enough examples at the QA developer's disposal to study them, we decided to build an annotated corpus.

Cet article présente une série d’évaluations visant à étudier l’apport d’une analyse syntaxique robuste des questions et des documents dans un système de questions-réponses. Ces évaluations ont été effectuées sur le système FIDJI, qui utilise à la fois des informations syntaxiques et des techniques plus “traditionnelles”. La sélection des documents, l’extraction de la réponse ainsi que le comportement selon les différents types de questions ont été étudiés.

Recent years have seen increasing attention in temporal processing of texts as well as a lot of standardization effort of temporal information in natural language. A central part of this information lies in the temporal relations between events described in a text, when their precise times or dates are not known. Reliable human annotation of such information is difficult, and automatic comparisons must follow procedures beyond mere precision-recall of local pieces of information, since a coherent picture can only be considered at a global level. We address the problem of evaluation metrics of such information, aiming at fair comparisons between systems, by proposing some measures taking into account the globality of a text.

Cet article traite de l’annotation automatique d’informations temporelles dans des textes et vise plus particulièrement les relations entre événements introduits par les verbes dans chaque clause. Si ce problème a mobilisé beaucoup de chercheurs sur le plan théorique, il reste en friche pour ce qui est de l’annotation automatique systématique (et son évaluation), même s’il existe des débuts de méthodologie pour faire réaliser la tâche par des humains. Nous proposons ici à la fois une méthode pour réaliser la tâche automatiquement et une manière de mesurer à quel degré l’objectif est atteint. Nous avons testé la faisabilité de ceci sur des dépêches d’agence avec des premiers résultats encourageants.