Empirical methods for exploiting parallel texts
Empirical methods for exploiting parallel texts
Natural Language Engineering
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Mining the Web for bilingual text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
An unsupervised method for word sense tagging using parallel corpora
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Word translation disambiguation using Bilingual Bootstrapping
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
An empirical study of the domain dependence of supervised word sense disambiguation systems
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Polysemy and sense proximity in the Senseval-2 test suite
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Sense discrimination with parallel corpora
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
English lexical sample task description
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Pattern learning and active feature selection for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
The Johns Hopkins SENSEVAL2 system descriptions
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Word sense disambiguation vs. statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Estimating class priors in domain adaptation for word sense disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
An equivalent pseudoword solution to Chinese word sense disambiguation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A DOM tree alignment model for mining parallel data from the web
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Combining clues for lexical level aligning using the null hypothesis approach
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Word sense disambiguation using sense examples automatically acquired from a second language
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Word-sense disambiguation for machine translation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Disambiguation of biomedical abbreviations
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Good neighbors make good senses: exploiting distributional similarity for unsupervised WSD
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Acquiring sense tagged examples using relevance feedback
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Word sense disambiguation using automatically translated sense examples
CrossLangInduction '06 Proceedings of the International Workshop on Cross-Language Knowledge Induction
Unsupervised multilingual learning for POS tagging
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Scaling up word sense disambiguation via parallel texts
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
SemEval-2007 task 11: English lexical sample task via English-Chinese parallel text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
NUS-PT: exploiting parallel texts for word sense disambiguation in the English all-words tasks
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
SemEval-2010 task 3: cross-lingual word sense disambiguation
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Word sense disambiguation with distribution estimation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Word sense disambiguation for all words without hard labor
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multilingual part-of-speech tagging: two unsupervised approaches
Journal of Artificial Intelligence Research
A collocation-based WSD model: RFR-SUM
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Multilingual lexicons for machine translation
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Disambiguation of ambiguous biomedical terms using examples generated from the UMLS Metathesaurus
Journal of Biomedical Informatics
Arabic named entity recognition: using features extracted from noisy data
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
SemEval-2010 task 3: Cross-lingual word sense disambiguation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Enhancing mention detection using projection via aligned corpora
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Cross language text classification by model translation and semi-supervised learning
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
A probabilistic model based on n-grams for bilingual word sense disambiguation
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Measuring historical word sense variation
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Word sense disambiguation with multilingual features
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
ParaSense or how to use parallel corpora for word sense disambiguation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
An evaluation and possible improvement path for current SMT behavior on ambiguous nouns
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
Unsupervised multilingual learning
Unsupervised multilingual learning
Unsupervised cross-lingual lexical substitution
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Correcting semantic collocation errors with L1-induced paraphrases
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The need for application-dependent WSD strategies: a case study in MT
PROPOR'06 Proceedings of the 7th international conference on Computational Processing of the Portuguese Language
A quick tour of word sense disambiguation, induction and related approaches
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
Unsupervised translation sense clustering
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Using senses in HMM word alignment
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Five languages are better than one: an attempt to bypass the data acquisition bottleneck for WSD
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Aligned-Parallel-Corpora Based Semi-Supervised Learning for Arabic Mention Detection
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora, which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task. Our investigation reveals that this method of acquiring sense-tagged data is promising. On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. Our analysis also highlights the importance of the issue of domain dependence in evaluating WSD programs.