OWNS: Cross-lingual word sense disambiguation using weighted overlap counts and wordnet based similarity measures

Authors:
Lipta Mahapatra;Meera Mohan;Mitesh M. Khapra;Pushpak Bhattacharyya
Affiliations:
Dharmsinh Desai University, Nadiad, India;Dharmsinh Desai University, Nadiad, India;Indian Institute of Technology Bombay, Powai, Mumbai, India;Indian Institute of Technology Bombay, Powai, Mumbai, India
Venue:
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Year:
2010

Citing 8
Cited 0

Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone

SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
A systematic comparison of various statistical alignment models

Computational Linguistics
The mathematics of statistical machine translation: parameter estimation

Computational Linguistics - Special issue on using large corpora: II
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach

ACL '96 Proceedings of the 34th 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
Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Using measures of semantic relatedness for word sense disambiguation

CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
SemEval-2010 task 3: Cross-lingual word sense disambiguation

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We report here our work on English French Cross-lingual Word Sense Disambiguation where the task is to find the best French translation for a target English word depending on the context in which it is used. Our approach relies on identifying the nearest neighbors of the test sentence from the training data using a pairwise similarity measure. The proposed measure finds the affinity between two sentences by calculating a weighted sum of the word overlap and the semantic overlap between them. The semantic overlap is calculated using standard Wordnet Similarity measures. Once the nearest neighbors have been identified, the best translation is found by taking a majority vote over the French translations of the nearest neighbors.