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1
A New Probabilistic LR Language Model for Statistical Parsing
In: ftp://ftp.cs.titech.ac.jp/lab/tanaka/papers/97/inui97a.ps.gz (1997)
Abstract: 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 first formalize our PLR model and enumerate some of its features. We then discuss the differences between Briscoe and Carroll's model and ours. We also qualitatively compare a model based on canonical LR with one based on lookahead LR. 1 Introduction The increasing availability of text corpora has been encouraging researchers to explore statistical approaches for various tasks in natural language processing. Statistical parsing is one of these approaches. In statistical parsing, one of the most straightforward methodologies is to.
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.4148
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2
Natural Language Analysis and Generation Technologies
In: ftp://ftp.cs.titech.ac.jp/pub/TR/93/TR93-0028.ps.gz (1993)
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3
Text Revision: A Model and Its Implementation
In: ftp://ftp.cs.titech.ac.jp/pub/TR/92/TR92-0011.ps.gz (1992)
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4
An Empirical Study on Statistical Disambiguation of Japanese Dependency Structures Using a Lexically Sensitive Language Model
In: http://galaga.jaist.ac.jp:8000/~kshirai/papers/shirai97d.pdf
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5
An Empirical Evaluation on Statistical Parsing of Japanese Sentences using Lexical Association Statistics
In: http://www.aclweb.org/anthology-new/W/W98/W98-1510.pdf
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6
Fine-grained Utterance Delimitation and Organization in Incremental Explanation Generation
In: ftp://ftp.cs.titech.ac.jp/lab/tanaka/papers/97/inui97d.ps.gz
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