Natural language parsing as statistical pattern recognition
Natural language parsing as statistical pattern recognition
Assigning function tags to parsed text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Definitional, personal, and mechanical constraints on part of speech annotation performance
Natural Language Engineering
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
Proceedings of the 20th ACM international conference on Information and knowledge management
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In the field of empirical natural language processing, researchers constantly deal with large amounts of marked-up data; whether the markup is done by the researcher or someone else, human nature dictates that it will have errors in it. This paper will more fully characterise the problem and discuss whether and when (and how) to correct the errors. The discussion is illustrated with specific examples involving function tagging in the Penn treebank.