Article published In:
TerminologyVol. 9:2 (2003) ► pp.201–219
Automatic term recognition based on statistics of compound nouns and their components
In this paper, we propose a new approach to enhance automatic recognition systems for domain-specific terms. The approach is based on the statistics about the relation between a compound noun and its constituents that are simple nouns. More precisely, we focus on how many nouns adjoin the noun in question to form compound nouns. We propose several scoring methods based on this approach and experimentally evaluate them on the NTCIR1 TMREC test collection. The results are very promising, especially in low and high recall.
Keywords: domain terminology, extraction, compound nouns, simple nouns, statistics
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2005. Terminology Extraction: An Analysis of Linguistic and Statistical Approaches. In Knowledge Mining [Studies in Fuzziness and Soft Computing, 185], ► pp. 255 ff.
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Spasic, I., S. Ananiadou & J. Tsujii
2005. MaSTerClass: a case-based reasoning system for the classification of biomedical terms. Bioinformatics 21:11 ► pp. 2748 ff.
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2013. Building a Bilingual Dictionary from a Japanese-Chinese Patent Corpus. In Computational Linguistics and Intelligent Text Processing [Lecture Notes in Computer Science, 7817], ► pp. 276 ff.
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