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

arXiv: https://arxiv.org/abs/1807.03012 · HTML: https://mlsanity.com/paper/1807.03012/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Sentence Structure and Word Relationship Modeling for Emphasis Selection](https://mlsanity.com/paper/2108.12750/) (2021)

## Abstract (arXiv)

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.