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Empirical Support for Probabilistic GLR Parsing

This paper discusses the e ectiveness of a new probabilistic generalized LR model (PGLR) in word-based parsing (morphological and syntactic analysis) tasks, in which we have to consider the word segmentation and multiple part-of-speech problems. Parsing a sentence from the morphological level makes the task much more complex because of the increase of parse ambiguity stemming from word segmentation ambiguities and multiple corresponding sequences of parts-of-speech. The experiments show that the PGLR model yields the best results comparing with the existing Briscoe and Carroll model (B&C) for GLR parsing, and \two-level PCFG", on experimentation on the ATR Japanese corpus.