Work

Neural Lemmatization of Multiword Expressions

Year: 2019

Type: conference-paper

Abstract: This article focuses on the lemmatization of multiword expressions (MWEs).We propose a deep encoder-decoder architecture generating for every MWE word its corresponding part in the lemma, based on the internal context of the MWE.The encoder relies on recurrent networks based on (1) the character sequence of the individual words to capture their morphological properties, and (2) the word sequence of the MWE to capture lexical and syntactic properties.The decoder in charge of generating the corresponding part of the lemma for each word of the MWE is based on a classical character-level attention-based recurrent model.Our model is evaluated for Italian, French, Polish and Portuguese and shows good performances except for Polish.

DOI: 10.18653/v1/w19-5117

Authors: Marine Schmitt, Mathieu Constant

Institutions: Centre National de la Recherche Scientifique, Analyse et Traitement Informatique de la Langue Française, Université de Lorraine

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funder: Agence Nationale de la Recherche

Awards: G1166465053, Syntactic parsing and multiword expressions in French, G6554659165

OpenAlex ID: W2970635698