Developing an Algorithm for Mining Semantics in Texts
نویسندگان
چکیده
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. keywords. semantics, algorithm, text pattern, probabilistic graphical model, semantic argument, word sense, NP chunk
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تاریخ انتشار 2012