Abstract
We present a novel semi-supervised incremental approach for discovering word categories, sets of words sharing a significant aspect of distributional context. We utilize high frequency words as seed in order to capture semantic information in form of symmetric similarity of word pair, lexical category is then created based on a new clustering algorithm proposed recently: affinity propagation (AP). Furthermore, we assess the performance using a new measure we proposed that meets three criteria: informativeness, diversity and purity. The quantitative and qualitative evaluation show that this semi-supervised incremental approach is plausible for induction of lexical categories from distributional data.
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Zhang, B., Wang, X. (2013). Semi-supervised Incremental Model for Lexical Category Acquisition. In: Du, Z. (eds) Proceedings of the 2012 International Conference of Modern Computer Science and Applications. Advances in Intelligent Systems and Computing, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33030-8_12
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DOI: https://doi.org/10.1007/978-3-642-33030-8_12
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