K. Erk and S. Pado and U. Pado. Corpus-driven Model of Regular and Inverse Selectional Preferences [preprint PDF]. Computational Linguistics 36(4), 723--763. doi:10.1162/coli_a_00017.
We present a vector space-based model for selectional preferences that predicts plausibility scores for argument head words. 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 with 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.
@Article{erk10:_corpus,
author = {Katrin Erk and Sebastian Pad\'o and Ulrike Pad\'o},
title = {A Flexible, Corpus-driven Model of Regular
and Inverse Selectional Preferences},
journal = {Computational Linguistics},
volume = {36},
number = {4},
year = 2010,
pages = {723--763}
}