Natural Language Engineering
Recognizing subjectivity: a case study in manual tagging
Natural Language Engineering
HLT '93 Proceedings of the workshop on Human Language Technology
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Relieving the data acquisition bottleneck in word sense disambiguation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Inter-coder agreement for computational linguistics
Computational Linguistics
Multilingual dependency analysis with a two-stage discriminative parser
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
A machine learning approach to textual entailment recognition
Natural Language Engineering
Investigations on word senses and word usages
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
A distributed database system for developing ontological and lexical resources in harmony
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Generating entailment rules from FrameNet
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
What is word meaning, really?: (and how can distributional models help us describe it?)
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Syntactic processing using the generalized perceptron and beam search
Computational Linguistics
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Low interannotator agreement (IAA) is a well-known issue in manual semantic tagging (sense tagging). IAA correlates with the granularity of word senses and they both correlate with the amount of information they give as well as with its reliability. We compare different approaches to semantic tagging in WordNet, FrameNet, PropBank and OntoNotes with a small tagged data sample based on the Corpus Pattern Analysis to present the reliable information gain (RG), a measure used to optimize the semantic granularity of a sense inventory with respect to its reliability indicated by the IAA in the given data set. RG can also be used as feedback for lexicographers, and as a supporting component of automatic semantic classifiers, especially when dealing with a very fine-grained set of semantic categories.