WordNet: a lexical database for English
Communications of the ACM
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Multiword Expressions: A Pain in the Neck for NLP
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Interleaving syntax and semantics in an efficient bottom-up parser
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Automatic identification of non-compositional phrases
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A survey on tree edit distance and related problems
Theoretical Computer Science
A statistical model for parsing and word-sense disambiguation
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
An empirical model of multiword expression decomposability
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Measuring the relative compositionality of verb-noun (V-N) collocations by integrating features
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Exploiting semantic information for HPSG parse selection
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Compositionality and multiword expressions: six of one, half a dozen of the other?
MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Automatic identification of non-compositional multi-word expressions using latent semantic analysis
MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Distinguishing subtypes of multiword expressions using linguistically-motivated statistical measures
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
Semantics-based multiword expression extraction
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
Detecting compositionality in multi-word expressions
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Parsing the penn chinese treebank with semantic knowledge
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Decreasing lexical data sparsity in statistical syntactic parsing: experiments with named entities
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
Multiword expressions in statistical dependency parsing
SPMRL '11 Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
An unsupervised ranking model for noun-noun compositionality
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Parsing models for identifying multiword expressions
Computational Linguistics
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There is significant evidence in the literature that integrating knowledge about multiword expressions can improve shallow parsing accuracy. We present an experimental study to quantify this improvement, focusing on compound nominals, proper names and adjective-noun constructions. The evaluation set of multiword expressions is derived from Word-Net and the textual data are downloaded from the web. We use a classification method to aid human annotation of output parses. This method allows us to conduct experiments on a large dataset of unannotated data. Experiments show that knowledge about multiword expressions leads to an increase of between 7.5% and 9.5% in accuracy of shallow parsing in sentences containing these multiword expressions.