Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
New Techniques for Disambiguation in Natural Language and Their Application to Biological Text
The Journal of Machine Learning Research
Discriminating among word senses using McQuitty's similarity analysis
NAACLstudent '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Name discrimination by clustering similar contexts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Improved Unsupervised Name Discrimination with Very Wide Bigrams and Automatic Cluster Stopping
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Unsupervised Discrimination of Person Names in Web Contexts
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Classifying Biomedical Abstracts Using Committees of Classifiers and Collective Ranking Techniques
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Improving name discrimination: a language salad approach
CrossLangInduction '06 Proceedings of the International Workshop on Cross-Language Knowledge Induction
Multilingual name disambiguation with semantic information
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Domain information for fine-grained person name categorization
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Large-scale cross-document coreference using distributed inference and hierarchical models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Unsupervised name ambiguity resolution using a generative model
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Offensive language detection using multi-level classification
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
AUTOMATIC ANNOTATION OF AMBIGUOUS PERSONAL NAMES ON THE WEB
Computational Intelligence
Accurate unsupervised joint named-entity extraction from unaligned parallel text
NEWS '12 Proceedings of the 4th Named Entity Workshop
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Previous work by Pedersen, Purandare and Kulkarni (2005) has resulted in an unsupervised method of name discrimination that represents the context in which an ambiguous name occurs using second order co–occurrence features. These contexts are then clustered in order to identify which are associated with different underlying named entities. It also extracts descriptive and discriminating bigrams from each of the discovered clusters in order to serve as identifying labels. These methods have been shown to perform well with English text, although we believe them to be language independent since they rely on lexical features and use no syntactic features or external knowledge sources. In this paper we apply this methodology in exactly the same way to Bulgarian, English, Romanian, and Spanish corpora. We find that it attains discrimination accuracy that is consistently well above that of a majority classifier, thus providing support for the hypothesis that the method is language independent.