Probabilistic techniques for phrase extraction |
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Institution: | 1. School of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China;2. Hebei Key Laboratory of Material Near-net Forming Technology, Hebei University of Science and Technology, Shijiazhuang 050018, China |
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Abstract: | This study proposes a probabilistic model for automatically extracting English noun phrases without part-of-speech tagging or any syntactic analysis. The technique is based on a Markov model, whose initial parameters are estimated by a phrase lookup program with a phrase dictionary, then optimized by a set of maximum entropy (ME) parameters for a set of morphological features. Using the Viterbi algorithm with the trained Markov model, the program can dynamically extract noun phrases from input text. Experiments show that this technique is of comparable effectiveness with the best existing techniques. |
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