Many applications in the context of natural language processing or information retrieval may be largely improved if they were able to fully exploit the rich semantic information annotated in high-quality, publicly available resources such as the FrameNet and the WordNet databases. Nevertheless, the practical use of similar resources is often biased by the limited coverage of semantic phenomena that they provide. A natural solution to this problem would be to automatically establish anchors between these resources that would allow us 1) to jointly use the encoded information, thus possibly overcoming limitations of the individual corpora, and 2) to extend each resource coverage by exploiting the information encoded in the others. In this paper, we present a supervised learning framework for the mapping of FrameNet lexical units onto WordNet synsets based on a reduced set of novel and semantically rich features. The automatically learnt mapping, which we call MapNet, can be used 1) to extend frame sets in the English FrameNet, 2) to populate frame sets in the Italian FrameNet via MultiWordNet and 3) to add frame labels to the MultiSemCor corpus. Our evaluation on these tasks shows that the proposed approach is viable and can result in accurate automatic annotations.
Sara REF presented a supervised learning framework for the mapping of FrameNet lexical units onto PWN synsets to solve limited coverage of semantic phenomena for NLP applications.
New Features for FrameNet - WordNet Mapping
CoNLL
Computer Science