Constructing a word similarity graph from vector based word representation for named entity recognition

College

College of Science

Department/Unit

Mathematics and Statistics Department

Document Type

Conference Proceeding

Source Title

WEBIST 2018 - Proceedings of the 14th International Conference on Web Information Systems and Technologies

First Page

166

Last Page

171

Publication Date

1-1-2018

Abstract

In this paper, we discuss a method for identifying a seed word that would best represent a class of named entities in a graphical representation of words and their similarities. Word networks, or word graphs, are representations of vectorized text where nodes are the words encountered in a corpus, and the weighted edges incident on the nodes represent how similar the words are to each other. Word networks are then divided into communities using the Louvain Method for community detection, then betweenness centrality of each node in each community is computed. The most central node in each community represents the most ideal candidate for a seed word of a named entity group which represents the community. Our results from our bilingual data set show that words with similar lexical content, from either language, belong to the same community. Copyright © 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

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Digitial Object Identifier (DOI)

10.5220/0006926201660171

Disciplines

Computer Sciences | Mathematics

Keywords

Linguistics—Graphic methods; Semantics—Mathematical models

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