Work

LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting

Year: 2017

Type: conference-paper

Abstract: This article describes our proposed system named LIM-LIG.This system is designed for SemEval 2017 Task1: Semantic Textual Similarity (Track1).LIM-LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Arabic sentences.The main idea is to exploit the word representations as vectors in a multidimensional space to capture the semantic and syntactic properties of words.IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence.LIM-LIG system achieves a Pearsons correlation of 0.74633, ranking 2nd among all participants in the Arabic monolingual pairs STS task organized within the Se-mEval 2017 evaluation campaign.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/s17-2017

Authors: El Moatez Billah NAGOUDI, Jérémy Ferrero, Didier Schwab

Institutions: Laboratoire d'Informatique de Grenoble, Université Grenoble Alpes

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2626047032

FWCI: 1.372

Cites: 13

Cited by: 17

Related to: 10