Constructing a Word Similarity Graph from Vector based Word Representation for Named Entity Recognition

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. We intend to build a bilingual word graph and identify seed words through community analysis that would be best used to segment a graph according to its named entities, therefore providing an unsupervised way of tagging named entities for a bilingual language base.

Sentence Structure andWord Relationship Modeling for Emphasis Selection

“…Currently, graph neural networks are applied to various tasks. Feria et al (Feria et al, 2018) construct a word graph by calculating the word embedding similarity and apply the community detection algorithm to find different communities. Through the graph, they can find named entities for a bilingual language base in an 1561 unsupervised manner.…”

Sentence Structure and Word Relationship Modeling for Emphasis Selection

“…Currently, graph neural networks are applied to various tasks. Feria et al (Feria et al, 2018) construct a word graph by calculating the word embedding similarity and apply the community detection algorithm to find different communities. Through the graph, they can find named entities for a bilingual language base in an unsupervised manner.…”