The role of domain information in Word Sense Disambiguation
Natural Language Engineering
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Domain kernels for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
From predicting predominant senses to local context for word sense disambiguation
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
| Hi-index | 0.00 |
An unsupervised methodology for Word Sense Disambiguation, called Dynamic Domain Sense Tagging, is presented. It relies on the convergence of two very well known unsupervised approaches (i.e. Domain Driven Disambiguation and Conceptual Density). For each target word a domain is dynamically modeled by expanding the its topical context, i.e. a set of words evoking the underlying/implict domain where the word is located. The estimation of the paradigmatic similarity within such a specific lexicon is assumed as a disambiguation model. The Conceptual Density measure is here used to account for paradigmatic associations, and the top scored senses of the target word are selected accordingly. Results confirm the impact of domain based representation in capturing useful paradigmatic generalizations, especially when small text fragments are available. In addition, the precision/recall tradeoff of the resulting method can be tuned in a meaningful way, allowing us to achieve impressively high precision scores in a purely unsupervised setting.