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
Propositionalization approaches to relational data mining
Relational Data Mining
A Baseline Methodology for Word Sense Disambiguation
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
The grammar of sense: Using part-of-speech tags as a first step in semantic disambiguation
Natural Language Engineering
Principle-based parsing without overgeneration
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
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
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
Randomised restarted search in ILP
Machine Learning
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
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
SemEval-2007 task 17: English lexical sample, SRL and all words
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
NUS-ML: improving word sense disambiguation using topic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UBC-ALM: combining k-NN with SVD for WSD
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
USP-IBM-1 and USP-IBM-2: the ILP-based systems for lexical sample WSD in SemEval-2007
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Change of representation for statistical relational learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Support vector inductive logic programming
DS'05 Proceedings of the 8th international conference on Discovery Science
Data-based research at IIT Bombay
ACM SIGMOD Record
Hi-index | 0.00 |
Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.