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In this paper, we discuss a method for identifying a seed word that wouldbest represent a class of named entities in a graphical representation of wordsand their similarities. Word networks, or word graphs, are representations ofvectorized text where nodes are the words encountered in a corpus, and theweighted edges incident on the nodes represent how similar the words are toeach other. We intend to build a bilingual word graph and identify seed wordsthrough community analysis that would be best used to segment a graph accordingto its named entities, therefore providing an unsupervised way of tagging namedentities for a bilingual language base.

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