Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Self-organized language modeling for speech recognition
Readings in speech recognition
Robust learning, smoothing, and parameter tying on syntactic ambiguity resolution
Computational Linguistics
Automatic Ambiguity Resolution in Natural Language Processing: An Empirical Approach
Automatic Ambiguity Resolution in Natural Language Processing: An Empirical Approach
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
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In this paper, we propose a method for constructing bigram LR tables by way of incorporating bigram constraints into an LR table. Using a bigram LR table, it is possible for a GLR parser to make use of both bigram and CFG constraints in natural language processing. Applying bigram LR tables to our GLR method has the following advantages: (1) Language models utilizing bigram LR tables have lower perplexity than simple bigram language models, since local constraints (bigram) and global constraints (CFG) are combined in a single bigram LR table. (2) Bigram constraints are easily acquired from a given corpus. Therefore data sparseness is not likely to arise. (3) Separation of local and global constraints keeps down the number of CFG rules. The first advantage leads to a reduction in complexity, and as the result, better performance in GLR parsing. Our experiments demonstrate the effectiveness of our method.