Foundations of statistical natural language processing
Foundations of statistical natural language processing
Tree-bank Grammars
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Linguistically-motivated grammar extraction, generalization and adaptation
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Preparation of knowledge bank is a very difficult task. In this paper, we discuss the knowledge extraction from the manually examined Sinica Treebank. Categorical information, word-to-word relation, word collocations, new syntactic patterns and sentence structures are obtained. A searching system for Chinese sentence structure was developed in this study. By using pre-extracted data and SQL commands, the system replies the user’s queries efficiently. We also analyze the extracted grammars to study the tradeoffs between the granularity of the grammar rules and their coverage as well as ambiguities. It provides the information of knowing how large a treebank is sufficient for the purpose of grammar extraction. Finally, we also analyze the tradeoffs between grammar coverage and ambiguity by parsing results from the grammar rules of different granularity.