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Natural Language Processing and Cognitive Science by Michael Zock, Mai Yanagimura, Elena Yagunova, Svetlana Volskaya, Jordi Turmo, Susanna Tron, Rocco Tripodi, Radu Topor, Ludovic Tanguy, David Suendermann-Oeft, Elizabeth Rosenfeld, Horacio Rodriguez, Livio Robaldo, Solange O. Rezende, Michaela Regneri, Lidia Pivovarova, Marcello Pelillo, Catherine Pelachaud, Thiago S. Pardo, Ali M. Naderi, Sachiyo Muranishi, Michael Muck, Martin Mory, Francois Morlane-Hondere, Tarek Mehrez, Wieslaw Lubaszewski, Patrick Lange, Shingo Kuroiwa, Manfred Klenner, Diane King, Mohamed Hamed Kholief, Alexei V. Ivanov, Yasuo Horiuchi, Nora Hollenstein, Myriam Hernandez Alvarez, Jiri Havelka, Nabil Hathout, Marcin Hareza, Jose M. Gomez, Emiliano Giovannetti, Nadine Glas, Daniela Gifu, Izabela Gatkowska, Jean-Gabriel Ganascia, Daisuke Furukawa, Richard Frost, Cécile Fabre, Ahmed Magdy Ezzeldin, Yasser El-Sonbaty, Luigi Di Caro, Matthew Crocker, Dan Cristea, Jan Curin, Conrado S. Merley, Pawel Chrzazcz, Jesus Calvillo, Mohamed Amine Boukhaled, Guido Boella, Jared Bernstein, Alessia Bellusci, Andrea Bellandi, Raimo Bakis, Eniafe Festus Ayetiran, Michael Amsler, Nabil Abdullah, César Aguilar, Olga Acosta, Rodolfo Delmonte, Bernadette Sharp

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Franaçois Morlane-Hondère, Cécile Fabre, Nabil Hathout, and Ludovic Tanguy

Disambiguating Distributional Neighbors Using a Lexical Substitution Dataset

Abstract: This paper addresses the issue of polysemy in a distributional thesaurus. In such resources, distributional neighbors can relate indistinguishably to various senses. We propose a method to cluster the neighbors of a target word with respect to its senses, i.e. to attribute one sense to each neighbor. This is made possible by the use of a lexical substitution dataset, to which the distribution of the neighbors are compared.

1 Introduction

Many NLP applications need to know whether a word A is semantically more related to B than to C. Unsupervised corpus-based approaches to similarity ...

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