Sabine Loudcher


2017

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Détection automatique de métaphores dans des textes de Géographie : une étude prospective (Automatic detection of metaphors in Geographical research papers : a prospective study)
Max Beligné | Aleksandra Campar | Jean-Hugues Chauchat | Melanie Lefeuvre | Isabelle Lefort | Sabine Loudcher | Julien Velcin
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

Cet article s’intègre dans un projet collaboratif qui vise à réaliser une analyse longitudinale de la production universitaire en Géographie. En particulier, nous présentons les premiers résultats de l’application d’une méthode de détection automatique de métaphores basée sur les modèles de thématiques latentes. Une analyse détaillée permet de mieux comprendre l’impact de certains choix et de réfléchir aux pistes de recherche que nous serons amenés à explorer pour améliorer ces résultats.

2016

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Hypergraph Modelization of a Syntactically Annotated English Wikipedia Dump
Edmundo Pavel Soriano Morales | Julien Ah-Pine | Sabine Loudcher
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Wikipedia, the well known internet encyclopedia, is nowadays a widely used source of information. To leverage its rich information, already parsed versions of Wikipedia have been proposed. We present an annotated dump of the English Wikipedia. This dump draws upon previously released Wikipedia parsed dumps. Still, we head in a different direction. In this parse we focus more into the syntactical characteristics of words: aside from the classical Part-of-Speech (PoS) tags and dependency parsing relations, we provide the full constituent parse branch for each word in a succinct way. Additionally, we propose a hypergraph network representation of the extracted linguistic information. The proposed modelization aims to take advantage of the information stocked within our parsed Wikipedia dump. We hope that by releasing these resources, researchers from the concerned communities will have a ready-to-experiment Wikipedia corpus to compare and distribute their work. We render public our parsed Wikipedia dump as well as the tool (and its source code) used to perform the parse. The hypergraph network and its related metadata is also distributed.

2013

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AMI&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis
Mohamed Dermouche | Leila Khouas | Julien Velcin | Sabine Loudcher
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)