Foundations of statistical natural language processing
Foundations of statistical natural language processing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Distributional part-of-speech tagging
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Combining distributional and morphological information for part of speech induction
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Detecting errors in part-of-speech annotation
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Part of speech tagging in context
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Categorizing local contexts as a step in grammatical category induction
CACLA '09 Proceedings of the EACL 2009 Workshop on Cognitive Aspects of Computational Language Acquisition
Correcting a PoS-tagged corpus using three complementary methods
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Consistency checking for Treebank alignment
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
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As it serves as a basis for POS tagging, category induction, and human category acquisition, we investigate the information needed to disambiguate a word in a local context, when using corpus categories. Specifically, we increase the recall of an error detection method by abstracting the word to be disambiguated to a representation containing information about some of its inherent properties, namely the set of categories it can potentially have. This work thus provides insights into the relation of corpus categories to categories derived from local contexts.