Yue Ma


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An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans.
Sanjay Kamath | Brigitte Grau | Yue Ma
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering

BIOASQ Task B Phase B challenge focuses on extracting answers from snippets for a given question. The dataset provided by the organizers contains answers, but not all their variants. Henceforth a manual annotation was performed to extract all forms of correct answers. This article shows the impact of using all occurrences of correct answers for training on the evaluation scores which are improved significantly.


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Ontology-based Technical Text Annotation
François Lévy | Nadi Tomeh | Yue Ma
Proceedings of the COLING Workshop on Synchronic and Diachronic Approaches to Analyzing Technical Language


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Semantic Annotation in Specific Domains with rich Ontologies (Annotation sémantique pour des domaines spécialisés et des ontologies riches) [in French]
Yue Ma | François Lévy | Adeline Nazarenko
Proceedings of TALN 2013 (Volume 1: Long Papers)


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Formal Description of Resources for Ontology-based Semantic Annotation
Yue Ma | Adeline Nazarenko | Laurent Audibert
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Ontology-based semantic annotation aims at putting fragments of a text in correspondence with proper elements of an ontology such that the formal semantics encoded by the ontology can be exploited to represent text interpretation. In this paper, we formalize a resource for this goal. The main difficulty in achieving good semantic annotations consists in identifying fragments to be annotated and labels to be associated with them. To this end, our approach takes advantage of standard web ontology languages as well as rich linguistic annotation platforms. This in turn is concerned with how to formalize the combination of the ontological and linguistical information, which is a topical issue that has got an increasing discussion recently. Different from existing formalizations, our purpose is to extend ontologies by semantic annotation rules whose complexity increases along two dimensions: the linguistic complexity and the rule syntactic complexity. This solution allows reusing best NLP tools for the production of various levels of linguistic annotations. It also has the merit to distinguish clearly the process of linguistic analysis and the ontological interpretation.


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Détection des contradictions dans les annotations sémantiques
Yue Ma | Laurent Audibert
Actes de la 16ème conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

L’annotation sémantique a pour objectif d’apporter au texte une représentation explicite de son interprétation sémantique. Dans un précédent article, nous avons proposé d’étendre les ontologies par des règles d’annotation sémantique. Ces règles sont utilisées pour l’annotation sémantique d’un texte au regard d’une ontologie dans le cadre d’une plate-forme d’annotation linguistique automatique. Nous présentons dans cet article une mesure, basée sur la valeur de Shapley, permettant d’identifier les règles qui sont sources de contradiction dans l’annotation sémantique. Par rapport aux classiques mesures de précision et de rappel, l’intérêt de cette mesure est de ne pas nécessiter de corpus manuellement annoté, d’être entièrement automatisable et de permettre l’identification des règles qui posent problème.