Processing Metonymy: a Domain-Model Heuristic Graph Traversal Approach

We address here the treatment of metonymic expressions from a knowledge representation perspective, that is, in the context of a text understanding system which aims to build a conceptual representation from texts according to a domain model expressed in a knowledge representation formalism. We focus in this paper on the part of the semantic analyser which deals with semantic composition. We explain how we use the domain model to handle metonymy dynamically, and more generally, to underlie semantic composition, using the knowledge descriptions attached to each concept of our ontology as a kind of concept-level, multiple-role qualia structure. We rely for this on a heuristic path search algorithm that exploits the graphic aspects of the conceptual graphs formalism. The methods described have been implemented and applied on French texts in the medical domain.Comment: 6 pages, LaTeX, one encapsulated PostScript figure, uses colap.sty (included) and epsf.sty (available from the cmp-lg macro library). To appear in Coling-9

LIMSI@ CLEF eHealth 2015-task 2.

International audienceThis paper presents LIMSI’s participation in the User-Centered Health Information Retrieval task (task 2) at the CLEF eHealth 2015 workshop. In our contribution we explored two different strategies to query expansion, i.e. one based on entity recognition using MetaMap and the UMLS, and a second strategy based on disease hypothesis generation using self-constructed external resources such a corpus of Wikipedia pages describing diseases and conditions, and web pages from the Medline Plus health portal. Our best-scoring run was a weighed UMLS-based run which put emphasis on incorporating signs and symptoms recognized in the topic text by MetaMap. This run achieved a P@10 score of 0.262 and nDCG@10 of 0.196, respectively

Accès mesurés aux sens

On rencontre un besoin croissant d’accès sémantique robuste à des données textuelles volumineuses et hétérogènes. Nous présentons ici en trois grands types les méthodes qui aident à obtenir cet accès, et qui s’appliquent aux mots comme aux textes : découper en unités porteuses de sens, partitionner pour obtenir des catégories thématiques ou sémantiques, et répartir dans des classes prédéfinies.There is a growing need for robust semantic access to large, heterogeneous textual data. We present here under three categories the methods which help to achieve such an access, and which apply both to words and to texts : segmenting into meaning-bearing units, partitioning to obtain thematic or semantic categories, and distributing into predefined classes.Se necesita cada vez más un ecceso semántico a datos textuales voluminosos y heterogéneos que sea robusto. Presentamos aquí tres grandes tipos de métodos que favorecen la obtención a este acceso y que se aplican tanto a los textos como a las palabras : recortar en unidades que transportan el sentido, particionar para obtener categorías temáticas o semánticas, y distribuir por clases predefinidas

Structured Named Entities in two distinct press corpora: Contemporary Broadcast News and Old Newspapers

International audienceThis paper compares the reference annotation of structured named entities in two corpora with different origins and properties. It ad- dresses two questions linked to such a comparison. On the one hand, what specific issues were raised by reusing the same annotation scheme on a corpus that differs from the first in terms of media and that predates it by more than a century? On the other hand, what contrasts were observed in the resulting annotations across the two corpora

Proposal for an Extension of Traditional Named Entitites: from Guidelines to Evaluation, an Overview

International audienceWithin the framework of the construction of a fact database, we defined guidelines to extract named entities, using a taxonomy based on an extension of the usual named entities defini- tion. We thus defined new types of entities with broader coverage including substantive- based expressions. These extended named en- tities are hierarchical (with types and compo- nents) and compositional (with recursive type inclusion and metonymy annotation). Human annotators used these guidelines to annotate a 1.3M word broadcast news corpus in French. This article presents the definition and novelty of extended named entity annotation guide- lines, the human annotation of a global corpus and of a mini reference corpus, and the evalu- ation of annotations through the computation of inter-annotator agreement. Finally, we dis- cuss our approach and the computed results, and outline further work

Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's Perspective

In this work, we present the first corpus for German Adverse Drug Reaction (ADR) detection in patient-generated content. The data consists of 4,169 binary annotated documents from a German patient forum, where users talk about health issues and get advice from medical doctors. As is common in social media data in this domain, the class labels of the corpus are very imbalanced. This and a high topic imbalance make it a very challenging dataset, since often, the same symptom can have several causes and is not always related to a medication intake. We aim to encourage further multi-lingual efforts in the domain of ADR detection and provide preliminary experiments for binary classification using different methods of zero- and few-shot learning based on a multi-lingual model. When fine-tuning XLM-RoBERTa first on English patient forum data and then on the new German data, we achieve an F1-score of 37.52 for the positive class. We make the dataset and models publicly available for the community.Comment: Accepted at LREC 202

Collecte orientée sur le Web pour la recherche d'information spécialisée

Les moteurs de recherche verticaux, qui se concentrent sur des segments spécifiques du Web, deviennent aujourd'hui de plus en plus présents dans le paysage d'Internet. Les moteurs de recherche thématiques, notamment, peuvent obtenir de très bonnes performances en limitant le corpus indexé à un thème connu. Les ambiguïtés de la langue sont alors d'autant plus contrôlables que le domaine est bien ciblé. De plus, la connaissance des objets et de leurs propriétés rend possible le développement de techniques d'analyse spécifiques afin d'extraire des informations pertinentes.Dans le cadre de cette thèse, nous nous intéressons plus précisément à la procédure de collecte de documents thématiques à partir du Web pour alimenter un moteur de recherche thématique. La procédure de collecte peut être réalisée en s'appuyant sur un moteur de recherche généraliste existant (recherche orientée) ou en parcourant les hyperliens entre les pages Web (exploration orientée).Nous étudions tout d'abord la recherche orientée. Dans ce contexte, l'approche classique consiste à combiner des mot-clés du domaine d'intérêt, à les soumettre à un moteur de recherche et à télécharger les meilleurs résultats retournés par ce dernier.Après avoir évalué empiriquement cette approche sur 340 thèmes issus de l'OpenDirectory, nous proposons de l'améliorer en deux points. En amont du moteur de recherche, nous proposons de formuler des requêtes thématiques plus pertinentes pour le thème afin d'augmenter la précision de la collecte. Nous définissons une métrique fondée sur un graphe de cooccurrences et un algorithme de marche aléatoire, dans le but de prédire la pertinence d'une requête thématique. En aval du moteur de recherche, nous proposons de filtrer les documents téléchargés afin d'améliorer la qualité du corpus produit. Pour ce faire, nous modélisons la procédure de collecte sous la forme d'un graphe triparti et appliquons un algorithme de marche aléatoire biaisé afin d'ordonner par pertinence les documents et termes apparaissant dans ces derniers.Dans la seconde partie de cette thèse, nous nous focalisons sur l'exploration orientée du Web. Au coeur de tout robot d'exploration orientée se trouve une stratégie de crawl qui lui permet de maximiser le rapatriement de pages pertinentes pour un thème, tout en minimisant le nombre de pages visitées qui ne sont pas en rapport avec le thème. En pratique, cette stratégie définit l'ordre de visite des pages. Nous proposons d'apprendre automatiquement une fonction d'ordonnancement indépendante du thème à partir de données existantes annotées automatiquement.Vertical search engines, which focus on a specific segment of the Web, become more and more present in the Internet landscape. Topical search engines, notably, can obtain a significant performance boost by limiting their index on a specific topic. By doing so, language ambiguities are reduced, and both the algorithms and the user interface can take advantage of domain knowledge, such as domain objects or characteristics, to satisfy user information needs.In this thesis, we tackle the first inevitable step of a all topical search engine : focused document gathering from the Web. A thorough study of the state of art leads us to consider two strategies to gather topical documents from the Web: either relying on an existing search engine index (focused search) or directly crawling the Web (focused crawling).The first part of our research has been dedicated to focused search. In this context, a standard approach consists in combining domain-specific terms into queries, submitting those queries to a search engine and down- loading top ranked documents. After empirically evaluating this approach over 340 topics, we propose to enhance it in two different ways: Upstream of the search engine, we aim at formulating more relevant queries in or- der to increase the precision of the top retrieved documents. To do so, we define a metric based on a co-occurrence graph and a random walk algorithm, which aims at predicting the topical relevance of a query. Downstream of the search engine, we filter the retrieved documents in order to improve the document collection quality. We do so by modeling our gathering process as a tripartite graph and applying a random walk with restart algorithm so as to simultaneously order by relevance the documents and terms appearing in our corpus.In the second part of this thesis, we turn to focused crawling. We describe our focused crawler implementation that was designed to scale horizontally. Then, we consider the problem of crawl frontier ordering, which is at the very heart of a focused crawler. Such ordering strategy allows the crawler to prioritize its fetches, maximizing the number of in-domain documents retrieved while minimizing the non relevant ones. We propose to apply learning to rank algorithms to efficiently order the crawl frontier, and define a method to learn a ranking function from existing crawls.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Adaptation of LIMSI's QALC for QA4MRE.

International audienceIn this paper, we present LIMSI participation to one of the pilot tasks of QA4MRE at CLEF 2012: Machine Reading of Biomedical Texts about Alzheimer. For this exercise, we adapted an existing question answering (QA) system, QALC, by searching answers in the reading document. This basic version was used for the evaluation and obtains 0.2, which was increased to 0.325 after basic corrections. We developed then different methods for choosing an answer, based on the expected answer type and the question plus answer rewritten to form hypothesis compared with candidates sentences. We also conducted studies on relation extraction by using an existing system. The last version of our system obtains 0.375