Semantic interpretation and the resolution of ambiguity
Semantic interpretation and the resolution of ambiguity
A connectionist approach to word sense disambiguation
A connectionist approach to word sense disambiguation
Using multiple knowledge sources for word sense discrimination
Computational Linguistics
Inductive logic programming: derivations, successes and shortcomings
ACM SIGART Bulletin
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
A Baseline Methodology for Word Sense Disambiguation
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Inductive Logic Programming for Natural Language Processing
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
A hybrid relational approach for WSD: first results
COLING ACL '06 Proceedings of the 21st International Conference on computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Feature Construction Using Theory-Guided Sampling and Randomised Search
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Interactive relational reinforcement learning of concept semantics
Machine Learning
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The identification of the correct sense of a word is necessary for many tasks in automatic natural language processing like machine translation, information retrieval, speech and text processing. Automatic Word Sense Disambiguation (WSD) is difficult and accuracies with state-of-the art methods are substantially lower than in other areas of text understanding like part-of-speech tagging. One shortcoming of these methods is that they do not utilize substantial sources of background knowledge, such as semantic taxonomies and dictionaries, which are now available in electronic form (the methods largely use shallow syntactic features). Empirical results from the use of Inductive Logic Programming (ILP) have repeatedly shown the ability of ILP systems to use diverse sources of background knowledge. In this paper we investigate the use of ILP for WSD in two different ways: (a) as a stand-alone constructor of models for WSD; and (b) to build interesting features, which can then be used by standard model-builders such as SVM. In our experiments we examine a monolingual WSD task using the 32 English verbs contained in the SENSEVAL-3 benchmark data; and a bilingual WSD task using 7 highly ambiguous verbs in machine translation from English to Portuguese. Background knowledge available is from eight sources that provide a wide range of syntactic and semantic information. For both WSD tasks, experimental results show that ILP-constructed models and models built using ILP-generated features have higher accuracies than those obtained using a state-of-the art feature-based technique equipped with shallow syntactic features. This suggests that the use of ILP with diverse sources of background knowledge can provide one way for making substantial progress in the field of automatic WSD.