Automatic Term Extraction Using Log-Likelihood Based Comparison with General Reference Corpus

Abstract. In the paper we present a method that allows an extraction of singleword terms for a specific domain. At the next stage these terms can be used as candidates for multi-word term extraction. The proposed method is based on comparison with general reference corpus using log-likelihood similarity. We also perform clustering of the extracted terms using k-means algorithm and cosine similarity measure. We made experiments using texts of the domain of computer science. The obtained term list is analyzed in detail.

• Type: Conference
• Year: 2010
• Where: NLDB
• Authors: Alexander F. Gelbukh, Grigori Sidorov, Eduardo Lavin-Villa, Liliana Chanona-Hernández
• Keywords: General Reference Corpus, Multi-word Term Extraction, Natural Language Processing, NLDB 2010, Singleword Terms