AUTOMATIC TERM EXTRACTION AND DOCUMENT SIMILARITY IN SPECIAL TEXT CORPORA

摘要

This paper confirms that the performance of a state-of-the-art automatic term extraction method on a computer science corpus is similar to previously published performance data on a medical corpus. The extracted terms are then used to estimate the similarity of papers in the computer science corpus using the standard Vector Space Model. The precision of retrieval using a term-based representation is compared with that of a word-based representation, and a link-based similarity metric based on the overlap of the local neighborhoods of the papers in the citation graph. The term-based approach offers comparable performance to the word-based approach, but potentially with a much smaller vocabulary size.