Multiword Expressions: A Pain in the Neck for NLP
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
MWEs as non-propositional content indicators
MWE '04 Proceedings of the Workshop on Multiword Expressions: Integrating Processing
Disambiguating Japanese compound verbs
Computer Speech and Language
Unsupervised type and token identification of idiomatic expressions
Computational Linguistics
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Verb noun construction MWE token supervised classification
MWE '09 Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications
Multi-word expression identification using sentence surface features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Handling sparsity for verb noun MWE token classification
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Linguistic cues for distinguishing literal and non-literal usages
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Design and analysis of genetic algorithm based Chinese keyword extracting
International Journal of Computer Applications in Technology
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Recognizing idioms in a sentence is important to sentence understanding. This paper discusses the lexical knowledge of idioms for idiom recognition. The challenges are that idioms can be ambiguous between literal and idiomatic meanings, and that they can be "transformed" when expressed in a sentence. However, there has been little research on Japanese idiom recognition with its ambiguity and transformations taken into account. We propose a set of lexical knowledge for idiom recognition. We evaluated the knowledge by measuring the performance of an idiom recognizer that exploits the knowledge. As a result, more than 90% of the idioms in a corpus are recognized with 90% accuracy.