Multiword Expressions: A Pain in the Neck for NLP
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IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Task-based evaluation of multiword expressions: a pilot study in statistical machine translation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Can recognising multiword expressions improve shallow parsing?
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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Coarse-to-fine natural language processing
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Syntax-Based Collocation Extraction
Syntax-Based Collocation Extraction
Inducing Tree-Substitution Grammars
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An n-gram frequency database reference to handle MWE extraction in NLP applications
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
Accommodating multiword expressions in an arabic LFG grammar
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Discriminative strategies to integrate multiword expression recognition and parsing
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Multiword expressions lie at the syntax/semantics interface and have motivated alternative theories of syntax like Construction Grammar. Until now, however, syntactic analysis and multiword expression identification have been modeled separately in natural language processing. We develop two structured prediction models for joint parsing and multiword expression identification. The first is based on context-free grammars and the second uses tree substitution grammars, a formalism that can store larger syntactic fragments. Our experiments show that both models can identify multiword expressions with much higher accuracy than a state-of-the-art system based on word co-occurrence statistics. We experiment with Arabic and French, which both have pervasive multiword expressions. Relative to English, they also have richer morphology, which induces lexical sparsity in finite corpora. To combat this sparsity, we develop a simple factored lexical representation for the context-free parsing model. Morphological analyses are automatically transformed into rich feature tags that are scored jointly with lexical items. This technique, which we call a factored lexicon, improves both standard parsing and multiword expression identification accuracy.