Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Word sense induction using word embeddings and community detection in complex networks

Full text
Author(s):
Correa, Jr., Edilson A. [1] ; Amancio, Diego R. [1, 2]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[2] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47408 - USA
Total Affiliations: 2
Document type: Journal article
Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 523, p. 180-190, JUN 1 2019.
Web of Science Citations: 0
Abstract

Word Sense Induction (WSI) is the ability to automatically induce word senses from corpora. The WSI task was first proposed to overcome the limitations of manually annotated corpus that are required in word sense disambiguation systems. Even though several works have been proposed to induce word senses, existing systems are still very limited in the sense that they make use of structured, domain-specific knowledge sources. In this paper, we devise a method that leverages recent findings in word embeddings research to generate context embeddings, which are embeddings containing information about the semantical context of a word. In order to induce senses, we modeled the set of ambiguous words as a complex network. In the generated network, two instances (nodes) are connected if the respective context embeddings are similar. Upon using well-established community detection methods to cluster the obtained context embeddings, we found that the proposed method yields excellent performance for the WSI task. Our method outperformed competing algorithms and baselines, in a completely unsupervised manner and without the need of any additional structured knowledge source. (C) 2019 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 14/20830-0 - Using complex networks to recognize patterns in written texts
Grantee:Diego Raphael Amancio
Support Opportunities: Regular Research Grants
FAPESP's process: 17/13464-6 - Modelling citation and information graphs: a complex network approach
Grantee:Diego Raphael Amancio
Support Opportunities: Scholarships abroad - Research
FAPESP's process: 16/19069-9 - Using semantical information to classify texts modelled as complex networks
Grantee:Diego Raphael Amancio
Support Opportunities: Regular Research Grants