| Abstract |
In this article we explore a methodology based on unsupervised learning that aims at automatically classifying the arguments of a verb, a classification crucially important in the process of sense isolation in lexicographic projects. More specifically, we strive to exploit the lexical semantic information obtained by combining a clustering algorithm with Word Embeddings and argue that the combination of both represents a fertile methodological ground for automatically classifying the selectional behaviour of a predicate. From the perspective of practical lexicography, our methodology should help to significantly shorten the time invested in isolating context-related senses of a predicate, in our case a verb. In particular, we illustrate, based on a case study of two Spanish verbs, how our methodology can provide a lexicographer with a general picture of a verb’s behaviour in a corpus and argue that the clusters we provide automatically encode insightful generalizations that facilitate the process of decision-making for a lexicographer as to which senses are most common for a particular predicate, according to the different types of contexts it is most frequently used in. |
| BibTex |
@InProceedings{ELX2016-092,
author={Alexandra Anna Spalek, Marco Del Tredici},
title={Automatically Identifying Selectional Restrictions of Predicates: A Computational Methodology for Practical Lexicography that Challenges Linguistic Theory},
pages={837-846},
booktitle={Proceedings of the 17th EURALEX International Congress},
year={2016},
month={sep},
date={6-10},
address={Tbilisi, Georgia},
editor={Tinatin Margalitadze, George Meladze},
publisher={Ivane Javakhishvili Tbilisi University Press},
isbn={978-9941-13-542-2},
} |
