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Relation, es-tu là ? Détection de relations par LSTM pour améliorer l’extraction de relations

Abstract : Various methods for relation extraction and classification have been proposed and benchmarked onstandard academic datasets. In real-life data however, the number of potential relations is enormousand the heuristics and count-based methods often used to separate actual relations from meaninglessco-occurrences fail to detect weak signals of importance. In this paper, we investigate the use of acomputationally-light binary detection model to identify whether a couple of entities in a sentencebears a relation, as a preliminary step prior to relation classification. Our model is based on the shortestdependency path between two entities analyzed with a LSTM recurrent network and combined withinformation on the entities types. On the binary relation detection task, we achieve results betterthan a state-of-the-art relation classification model adapted to detection with increased robustnessto relations unseen in training. We finally show that binary detection as a pre-processing step toclassification of relations is effective in significantly improving the latter.
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Submitted on : Tuesday, June 23, 2020 - 12:05:20 PM
Last modification on : Thursday, October 29, 2020 - 10:32:44 AM

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Cyrielle Mallart, Michel Le Nouy, Guillaume Gravier, Pascale Sébillot. Relation, es-tu là ? Détection de relations par LSTM pour améliorer l’extraction de relations. JEP-TALN-RECITAL 2020 - 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). Volume 2 : Traitement Automatique des Langues Naturelles, Jun 2020, Nancy, France. pp.279-287. ⟨hal-02784777v3⟩

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