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Title: The design of a statistical algorithm for resolving structural ambiguity in "V NP1 usde NP0?
Authors: Li, W 
Wong, KF
Keywords: Chinese language processing
Noun phrase extraction
Structural disambiguation
Issue Date: 2003
Publisher: Wiley-Blackwell
Source: Computational intelligence, 2003, v. 19, no. 1, p. 64-85 How to cite?
Journal: Computational intelligence 
Abstract: The existence of structural ambiguity in modifying clauses renders noun phrase (NP) extraction from running Chinese texts complicated. It is shown from previous experiments that nearly 33% of the errors in an NP extractor were actually caused by the use of clause modifiers. For example, consider the sequence “V + NP1 + 的(of ) + NP0.” It can be interpreted as two alternatives, a verb phrase (i.e., [V[NP1+的+ NP0]NP]VP) or a noun phrase (i.e., [[V NP1]VP+的+ NP0]NP). To resolve this ambiguity, syntactical, contextual, and semantics-based approaches are investigated in this article. The conclusion is that the problem can be overcome only when the semantic knowledge about words is adopted. Therefore, a structural disambiguation algorithm based on lexical association is proposed. The algorithm uses the semantic class relation between a word pair derived from a standard Chinese thesaurus,, to work out whether a noun phrase or a verb phrase has a stronger lexical association within the collocation. This can, in turn, determine the intended phrase structure. With the proposed algorithm, the best accuracy and coverage are 79% and 100%, respectively. The experiment also shows that the backed-off model is more effective for this purpose. With this disambiguation algorithm, parsing performance can be significantly improved.
ISSN: 0824-7935
EISSN: 1467-8640
DOI: 10.1111/1467-8640.00214
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