Extracting Clusters of Specialist Terms from Unstructured Text

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Gerow, Aaron. 2014. 'Extracting Clusters of Specialist Terms from Unstructured Text'. In: 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP '14). Doha, Qatar October 25-29, 2014. [Conference or Workshop Item]

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Official URL: http://emnlp2014.org/papers/pdf/EMNLP2014149.pdf

Abstract or Description

Automatically identifying related specialist terms is a difficult and important task required to understand the lexical structure of language. This paper develops a corpus-based method of extracting coherent clusters of satellite terminology — terms on the edge of the lexicon — using co-occurrence networks of unstructured text. Term clusters are identified by extracting communities in the co-occurrence graph, after which the largest is discarded and the remaining words are ranked by centrality within a community. The method is tractable on large corpora, requires no document structure and minimal normalization. The results suggest that the model is able to extract coherent groups of satellite terms in corpora with varying size, content and structure. The findings also confirm that language consists of a densely connected core (observed in dictionaries) and systematic, se mantically coherent groups of terms at the edges of the lexicon.

Item Type:

Conference or Workshop Item (Paper)

Identification Number (DOI):

https://doi.org/10.3115/v1/D14-1149