Abstract
Natural language processing (NLP) is a very hot research domain. One important branch of it is sentence analysis, including Chinese sentence analysis. However, currently, no mature deep analysis theories and techniques are available. An alternative way is to perform shallow parsing on sentences which is very popular in the domain. The chunk identification is a fundamental task for shallow parsing. The purpose of this paper is to characterize a chunk boundary parsing algorithm, using a statistical method combining adjustment rules, which serves as a supplement to traditional statistics-based parsing methods. The experimental results show that the model works well on the small dataset. It will contribute to the sequent processes like chunk tagging and chunk collocation extraction under other topics etc.
Original language | English |
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Title of host publication | PACLIC 20 - Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation |
Pages | 446-451 |
Number of pages | 6 |
Publication status | Published - 1 Dec 2006 |
Event | 20th Pacific Asia Conference on Language, Information and Computation, PACLIC 20 - Wuhan, China Duration: 1 Nov 2006 → 3 Nov 2006 |
Conference
Conference | 20th Pacific Asia Conference on Language, Information and Computation, PACLIC 20 |
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Country/Territory | China |
City | Wuhan |
Period | 1/11/06 → 3/11/06 |
ASJC Scopus subject areas
- Language and Linguistics
- Computer Science (miscellaneous)