A New Probabilistic LR Language Model for Statistical Parsing
نویسنده
چکیده
This paper presents a newly formalized probabilistic LR language model. Our model inherits its essential features from Briscoe and Carroll's generalized probabilistic LR (PLR) model [3], which obtains context-sensitivity by assigning a probability to each LR parsing action according to its left and right context. However, our model is simpler while maintaining a higher degree of context-sensitivity as compared to Briscoe and Carroll's model. In this paper, we rst formalize our PLR model and enumerate some of its features. We then discuss the di erences between Briscoe and Carroll's model and ours. We also qualitatively compare a model based on canonical LR with one based on lookahead LR.
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