SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Word sense disambiguation in queries
Proceedings of the 14th ACM international conference on Information and knowledge management
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
PageRank on semantic networks, with application to word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
| Hi-index | 0.00 |
For most Web searching applications, queries are commonly ambiguous because words usually contain several senses. Traditional Word Sense Disambiguation (WSD) methods use statistic models or ontology-based knowledge models to find the most appropriate sense for the ambiguous word. Since queries are usually short and may not provide enough context information for disambiguating queries, more than one appropriate interpretation for ambiguous queries may be found. Thus, it is not always reasonable for finding only one interpretation of the query. In this paper, we propose a cluster-based WSD method, which finds out all appropriate interpretations for the query. Because some senses of one ambiguous word usually have very close semantic relations, we may group those similar senses together for explaining the ambiguous word in one interpretation.