Constructing a Word Similarity Graph from Vector based Word Representation for Named Entity Recognition
Computation and Language
2018-07-13 v1 Information Retrieval
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.
Cite
@article{arxiv.1807.03012,
title = {Constructing a Word Similarity Graph from Vector based Word Representation for Named Entity Recognition},
author = {Miguel Feria and Juan Paolo Balbin and Francis Michael Bautista},
journal= {arXiv preprint arXiv:1807.03012},
year = {2018}
}
Comments
Preprint for 14th International Conference On Web Information Systems and Technologies