Abstract
The main disadvantage of collocation-based word sense disambiguation is that the recall is low, with relatively high precision. How to improve the recall without decrease the precision? In this paper, we investigate a word-class approach to extend the collocation list which is constructed from the manually sense-tagged corpus. But the word classes are obtained from a larger scale corpus which is not sense tagged. The experiment results have shown that the F-measure is improved to 71% compared to 54% of the baseline system where the word-class is not considered, although the precision decreases slightly. Further study discovers the relationship between the F-measure and the number of word-class trained from the various sizes of corpus.
Support by National Grant Fundamental Research 973 Program of China Under Grant No. 2004CB318102.
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Jin, P., Sun, X., Wu, Y., Yu, S. (2007). Word Clustering for Collocation-Based Word Sense Disambiguation . In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2007. Lecture Notes in Computer Science, vol 4394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70939-8_24
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DOI: https://doi.org/10.1007/978-3-540-70939-8_24
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