PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Corpus-driven Model of Regular and Inverse Selectional Preferences
Katrin Erk, Sebastian Pado and Ulrike Pado
Computational Linguistics Volume 36, Number 4, pp. 723-763, 2010. ISSN 0891-2017

Abstract

We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, plausibility scores for predicates given argument heads. We evaluate our model on one NLP task (pseudo-disambiguation) and one cognitive task (prediction of human plausibility judgments), gauging the influence of different parameters and comparing our model against other model classes. We obtain consistent benefits from using the disambiguation and semantic role information provided by a semantically tagged primary corpus. As for parameters, we identify settings that yield good performance across a range of experimental conditions. However, frequency remains a major influence of prediction quality, and we also identify more robust parameter settings suitable for applications with many infrequent items.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7322
Deposited By:Sebastian Pado
Deposited On:17 March 2011