Verb Sense Disambiguation Using Support Vector Machines: Impact of WordNet-Extracted Features
نویسندگان
چکیده
The disambiguation of verbs is usually considered to be more difficult with respect to other part-of-speech categories. This is due both to the high polysemy of verbs compared with the other categories, and to the lack of lexical resources providing relations between verbs and nouns. One of such resources is WordNet, which provides plenty of information and relationships for nouns, whereas it is less comprehensive with respect to verbs. In this paper we focus on the disambiguation of verbs by means of Support Vector Machines and the use of WordNet-extracted features, based on the hyperonyms of context nouns.
منابع مشابه
Selection Preference Basede Verb Sense Disambiguation Using WordNet
Selectional preferences are a source of linguistic information commonly applied to the task of Word Sense Disambiguation (WSD). To date, WSD systems using selectional preferences as the main disambiguation mechanism have achieved limited success. One possible reason for this limitation is the limited number of semantic roles used in the construction of selectional preferences. This study invest...
متن کاملGPLSI-IXA: Using Semantic Classes to Acquire Monosemous Training Examples from Domain Texts
This paper summarizes our participation in task #17 of SemEval–2 (All–words WSD on a specific domain) using a supervised class-based Word Sense Disambiguation system. Basically, we use Support Vector Machines (SVM) as learning algorithm and a set of simple features to build three different models. Each model considers a different training corpus: SemCor (SC), examples from monosemous words extr...
متن کاملLCC-WSD: System Description for English Coarse Grained All Words Task at SemEval 2007
This document describes the Word Sense Disambiguation system used by Language Computer Corporation at English Coarse Grained All Word Task at SemEval 2007. The system is based on two supervised machine learning algorithms: Maximum Entropy and Support Vector Machines. These algorithms were trained on a corpus created from SemCor, Senseval 2 and 3 all words and lexical sample corpora and Open Min...
متن کاملThe Role of Semantic Roles in Disambiguating Verb Senses
We describe an automatic Word Sense Disambiguation (WSD) system that disambiguates verb senses using syntactic and semantic features that encode information about predicate arguments and semantic classes. Our system performs at the best published accuracy on the English verbs of Senseval-2. We also experiment with using the gold-standard predicateargument labels from PropBank for disambiguating...
متن کاملExtensive Study on Automatic Verb Sense Disambiguation in Czech
In this paper we compare automatic methods for disambiguation of verb senses, in particular we investigate Näıve Bayes classifier, decision trees, and a rule-based method. Different types of features are proposed, including morphological, syntax-based, idiomatic, animacy, and WordNet-based features. We evaluate the methods together with individual feature types on two essentially different Czec...
متن کامل