Understanding noun compounds
Lexical semantic techniques for corpus analysis
Computational Linguistics - Special issue on using large corpora: II
Fast statistical parsing of noun phrases for document indexing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Semi-automatic recognition of noun modifier relationships
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Phrasal analysis of long noun sequences
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Toward treating English nominals correctly
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Corpus statistics meet the noun compound: some empirical results
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Algorithm for automatic interpretation of noun sequences
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Analysis of Japanese compound nouns using collocational information
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Effect of relationships between words on Japanese information retrieval
ACM Transactions on Asian Language Information Processing (TALIP)
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The syntactic structure of a nominal compound must be analyzed first for its semantic interpretation. In addition, the syntactic analysis of nominal compounds is very useful for NLP application such as information extraction, since a nominal compound often has a similar linguistic structure with a simple sentence, as well as representing concrete and compound meaning of an object with several nouns combined. In this paper, we present a novel model for structural analysis of nominal compounds using linguistic and statistical knowledge which is coupled based on lexical information. That is, the syntactic relations defined between nouns (complement-predicate and modifier-head relation) are obtained from large corpora and again used to analyze the structures of nominal compounds and identify the underlying relations between nouns. Experiments show that the model gives good results, and can be effectively used for application systems which do not require deep semantic information.