A Unified Method for Extracting Simple and Multiword Verbs with Valence Information and Application for Hungarian
Bálint Sass · Recent Advances in Natural Language Processing · 2009
We present a method for extracting verbcentered constructions (VCCs) from corpora. In our framework, simple and multiword verbs, with or without valence are all VCCs. They are treated uniformly, from e.g. to breathe till e.g. to take something into consideration. In order to extract VCCs we represent the corpus as a sequence of clauses that contain a verb together with all its NP dependents. The method is a generalization of a former subcategorization frame extraction method. It is based on cumulative counting of frequent subframes: small frequency counts are inherited to one of the longest available subframes using random selection. The method nds out automatically the number of elements in VCCs; and it detects automatically whether a content word is integral part of the VCC (forming a multiword verb), or just the verb-dependent relation is important (forming a valence slot of the verb). Signi cance of our method lies in its capability to deal with multiword verbs and (their) valence simultaneously. The paper includes evaluation for Hungarian, we obtain precision values above 80% using nbest lists evaluation. The representation and the method is in essence language independent, it could be applied to other languages as well.