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.
The work done by @cite_6 in identifying unsupervised approaches for constructing word similarity networks utilized vectorized corpora as a base dataset. They discussed the use of Princeton’s WordNet as a template for a word association network as their motivation. @cite_6 contributed to this model by measuring the information of a word’s co-occurrence with another word. Once the vectorized representation of the word’s cosine similarity score with another word is computed, they construct a similarity network where the weighted edges between words are selected above a specified threshold.
@cite_6
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To evaluate how much a pair of entities or documents are similar is a common problem for current applications. Most approaches for this problem are based on the co-occurrence. However, different terms or words may represent the same entity or similar semantic in the real world since a concept often has more than one way of expression. Existing works always focus on computing semantic relatedness of words. But relatedness cannot reflect the similarity most of the time, on the other hand, most of their corpus are from common data sources such as Wikipedia and are not useful for the specialized vocabulary. In this paper, we propose a novel unsupervised approach for evaluating the semantic similarity between words by mapping texts to vector space and computing prior information. In our approach, we construct a model that can identify the words representing the same entity in special context even though they don't belong to the same concept. At last, we construct a network of words in which paths between words can reflect the evolution process of concepts. Our experimental results show that that our approach gives an effective solution to discover the semantic relationship between words, especially for words in specialty domains.