4 research outputs found

    Extraction de patrons sémantiques appliquée à la classification d'Entités Nommées

    Get PDF
    International audienceLa variabilité des corpus constitue un problème majeur pour les systèmes de reconnaissance d'entités nommées. L'une des pistes possibles pour y remédier est l'utilisation d'approches linguistiques pour les adapter à de nouveaux contextes : la construction de patrons sémantiques peut permettre de désambiguïser les entités nommées en structurant leur environnement syntaxico-sémantique. Cet article présente une première réalisation sur un corpus de presse d'un système de correction. Après une étape de segmentation sur des critères discursifs de surface, le système extrait et pondère les patrons liés à une classe d'entité nommée fournie par un analyseur. Malgré des modèles encore relativement élémentaires, les résultats obtenus sont encourageants et montrent la nécessité d'un traitement plus approfondi de la classe Organisation. Abstract Corpus variation is a major problem for named entity recognition systems. One possible direction to tackle this problem involves using linguistic approaches to adapt them to unseen contexts : building semantic patterns may help for their disambiguation by structuring their syntactic and semantic environment. This article presents a preliminary implementation on a press corpus of a correction system. After a segmentation step based on surface discourse clues, the system extracts and weights the patterns linked to a named entity class provided by an analyzer. Despite relatively elementary models, the results obtained are promising and point on the necessary treatment of the Organisation class. Mots-clés : entités nommées, patrons sémantiques, segmentation discursive de surface Keywords: named entities, semantic patterns, surface discourse segmentation ISMAÏL EL MAAROUF, JEANNE VILLANEAU, SOPHIE ROSSE

    What’s missing in geographical parsing?

    Get PDF
    Geographical data can be obtained by converting place names from free-format text into geographical coordinates. The ability to geo-locate events in textual reports represents a valuable source of information in many real-world applications such as emergency responses, real-time social media geographical event analysis, understanding location instructions in auto-response systems and more. However, geoparsing is still widely regarded as a challenge because of domain language diversity, place name ambiguity, metonymic language and limited leveraging of context as we show in our analysis. Results to date, whilst promising, are on laboratory data and unlike in wider NLP are often not cross-compared. In this study, we evaluate and analyse the performance of a number of leading geoparsers on a number of corpora and highlight the challenges in detail. We also publish an automatically geotagged Wikipedia corpus to alleviate the dearth of (open source) corpora in this domain.We gratefully acknowledge the funding support of the Natural Environment Research Council (NERC) Ph.D. Studentship NE/M009009/1 (MG) and EPSRC (NC and NL: Grant No. EP/M005089/1
    corecore