The design of a statistical algorithm for resolving structural ambiguity in "V NP1 usde NP0"
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 + (sic) (of) + NP0." It can be interpreted as two alternatives, a verb phrase (i.e., [V[NP1 + (sic) + NP0](NP)](VP)) or a noun phrase (i.e., [[V NP1](VP) + (sic) + 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, [ (sic) ], 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.
structural disambiguation chinese language processing noun phrase extraction