A maximum entropy approach to natural language processing
Computational Linguistics
Word Sense vs. Word Domain Disambiguation: A Maximum Entropy Approach
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
The role of domain information in Word Sense Disambiguation
Natural Language Engineering
Ranking text units according to textual saliency, connectivity and topic aptness
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Word sense disambiguation using optimised combinations of knowledge sources
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Building a sense tagged corpus with open mind word expert
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Learning semantic classes for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The role of semantic roles in disambiguating verb senses
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Experiments in word domain disambiguation for parallel texts
WorkSense '00 Proceedings of the ACL-2000 Workshop on Word Senses and Multi-Linguality
Using domain information for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Content analysis for proactive intelligence: marshaling frame evidence
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A new minimally-supervised framework for domain word sense disambiguation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Hi-index | 0.00 |
Word subject domains have been widely used to improve the performance of word sense disambiguation algorithms. However, comparatively little effort has been devoted so far to the disambiguation of word subject domains. The few existing approaches have focused on the development of algorithms specific to word domain disambiguation. In this paper we explore an alternative approach where word domain disambiguation is achieved via word sense disambiguation. Our study shows that this approach yields very strong results, suggesting that word domain disambiguation can be addressed in terms of word sense disambiguation with no need for special purpose algorithms.