This paper presents an application of grammatical inference to the task of shallow parsing. We first learn a deterministic probabilistic automaton that models the joint distribution of Chunk (syntactic phrase) tags and Part-of-speech tags, and then use this automaton as a transducer to find the most likely chunk tag sequence using a dynamic programming algorithm. We discuss an efficient means of incorporating lexical information, which automatically identifies particular words that are useful using a mutual information criterion, together with an application of bagging that improve our results. Though the results are not as high as comparable techniques that use models with a fixed structure, the models we learn are very compact and efficient.
CITATION STYLE
Thollard, F., & Clark, A. (2002). Shallow parsing using probabilistic grammatical inference. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2484, pp. 269–282). Springer Verlag. https://doi.org/10.1007/3-540-45790-9_22
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