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Yet another ranking function for automatic multi-word term extraction

Lossio Ventura J.A., Jonquet C., Roche M., Teisseire M.. 2014. In : Adam Przepiórkowski, Maciej Ogrodniczuk (eds.). Proceedings of 9th International Conference on Natural Language Processing (NLP), PolTAL'2014, September 17-19, 2014, Warsaw, Poland. Cham : Springer International Publishing, p. 52-64. (Lecture Notes in Computer Science, 8686). International Conference on Natural Language Processing. 9, 2014-09-17/2014-09-19, Varsovie (Pologne).

DOI: 10.1007/978-3-319-10888-9_6

Term extraction is an essential task in domain knowledge acquisition. We propose two new measures to extract multiword terms from a domain-specific text. The first measure is both linguistic and statistical based. The second measure is graph-based, allowing assessment of the importance of a multiword term of a domain. Existing measures often solve some problems related (but not completely) to term extraction, e.g., noise, silence, low frequency, large-corpora, complexity of the multiword term extraction process. Instead, we focus on managing the entire set of problems, e.g., detecting rare terms and overcoming the low frequency issue. We show that the two proposed measures outperform precision results previously reported for automatic multiword extraction by comparing them with the state-of-the-art reference measures.

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