Learning Verbal Transitivity Using LogLinear Models
Abstract
In this paper we show how loglinear models can be used to cluster verbs based on their subcategorization preferences. We describe how the information about the phrases or clauses a verb goes with can be computationally learned from an automatically tagged corpus with 9,333,555 words. We will use loglinear modeling to describe the relation between the acquired counts for the part-of-speech tags co-occurring with the verbs on predetermined positions. Based on these results an unsupervised clustering algorithm will be proposed.
Cite
Text
Marques et al. "Learning Verbal Transitivity Using LogLinear Models." European Conference on Machine Learning, 1998. doi:10.1007/BFB0026667Markdown
[Marques et al. "Learning Verbal Transitivity Using LogLinear Models." European Conference on Machine Learning, 1998.](https://mlanthology.org/ecmlpkdd/1998/marques1998ecml-learning/) doi:10.1007/BFB0026667BibTeX
@inproceedings{marques1998ecml-learning,
title = {{Learning Verbal Transitivity Using LogLinear Models}},
author = {Marques, Nuno Miguel and Lopes, José Gabriel Pereira and Coelho, Carlos Agra},
booktitle = {European Conference on Machine Learning},
year = {1998},
pages = {19-24},
doi = {10.1007/BFB0026667},
url = {https://mlanthology.org/ecmlpkdd/1998/marques1998ecml-learning/}
}