Enriching a lexical semantic net with selectional preferences by means of statistical corpus analysis

Broad-coverage ontologies which represent lexical semantic knowledge are being built for more and more natural languages. Such resources provide very useful information for word sense disambiguation, which is crucial for a variety of NLP tasks (e.g. semantic annotation of corpora, information retrieval, or semantic inferencing). Since the manual encoding of such ontologies is very labour-intensive, the development of (semi-)automatic methods for acquiring lexical semantic information is an important task. This paper addresses the automatic acquisition of selectional preferences of verbs by means of statistical corpus analysis. Knowledge about such preferences is essential for inducing thematic relations, which link verbal concepts to nominal concepts that are selectionally preferred as their complements. Several approaches for learning selectional preferences from corpora have been proposed in the last years. However, their usefulness for ontology building is limited. This paper introduces a modification of one of these methods (i.e. the approach of Li & Abe [1]) and evaluates it by employing a gold standard. The results show that the modified approach is much more appropriate for the given task.