Abstract
This paper discusses an algorithm for identifying semantic arguments of a verb, word senses of a polysemous word, noun phrases in a sentence. The heart of the algorithm is a probabilistic graphical model. In contrast with other existed graphical models, such as Naive Bayes models, CRFs, HMMs, and MEMMs, this model determines a sequence of optimal class assignments among M choices for a sequence of N input symbols without using dynamic programming, running fast–O(MN), and taking less memory space–O(M). Experiments conducted on standard data sets show encourage results.
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Huang, M., Haralick, R.M. (2012). Developing an Algorithm for Mining Semantics in Texts. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_21
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DOI: https://doi.org/10.1007/978-3-642-28601-8_21
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