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A Probabilistic Model of Compound Nouns

by: Mark Lauer, and Mark Dras

In: Proceedings of the Seventh Australian Joint Conference on Artificial Intelligence AI94World Scientific (1994) , p. 474--481.

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URL:	http://arxiv.org/abs/cmp-lg/9409003

Compound nouns such as example noun compound are becoming more common in natural language and pose a number of difficult problems for NLP systems, notably increasing the complexity of parsing. In this paper we develop a probabilistic model for syntactically analysing such compounds. The model predicts compound noun structures based on knowledge of affinities between nouns, which can be acquired from a corpus. Problems inherent in this corpus-based approach are addressed: data sparseness is overcome by the use of semantically motivated word classes and sense ambiguity is explicitly handled in the model. An implementation based on this model is described in Lauer 1994 and correctly parses 77% of the test set.

BibTeX record

@inproceedings{Lauer_AJCAI94,
  abstract = {Compound nouns such as example noun compound are becoming more common in natural language and pose a number of difficult problems for NLP systems, notably increasing the complexity of parsing. In this paper we develop a probabilistic model for syntactically analysing such compounds. The model predicts compound noun structures based on knowledge of affinities between nouns, which can be acquired from a corpus. Problems inherent in this corpus-based approach are addressed: data sparseness is overcome by the use of semantically motivated word classes and sense ambiguity is explicitly handled in the model. An implementation based on this model is described in Lauer (1994) and correctly parses 77% of the test set.},
  added-at = {2009-08-17T00:43:39.000+0200},
  author = {Lauer, Mark and Dras, Mark},
  biburl = {http://www.bibsonomy.org/bibtex/25fe04ce048c3834ad88c9054bd02bf86/cbrewster},
  booktitle = {Proceedings of the Seventh Australian Joint Conference on Artificial Intelligence (AI94)},
  file = {Lauer_AJCAI94.pdf:Lauer_AJCAI94.pdf:PDF},
  interhash = {7bf3fbd73430a56f55bddcb30c445f6b},
  intrahash = {5fe04ce048c3834ad88c9054bd02bf86},
  keywords = {jabref:noKeywordAssigned},
  pages = {474--481},
  publisher = {World Scientific},
  timestamp = {2009-08-17T00:43:39.000+0200},
  title = {A Probabilistic Model of Compound Nouns},
  url = {http://arxiv.org/abs/cmp-lg/9409003},
  year = 1994
}

Endnote record

%0 Conference Paper
%1 Lauer_AJCAI94
%A Lauer, Mark
%A Dras, Mark
%B Proceedings of the Seventh Australian Joint Conference on Artificial Intelligence (AI94)
%D 1994
%I World Scientific
%K 
%P 474--481
%T A Probabilistic Model of Compound Nouns
%U http://arxiv.org/abs/cmp-lg/9409003
%X Compound nouns such as example noun compound are becoming more common in natural language and pose a number of difficult problems for NLP systems, notably increasing the complexity of parsing. In this paper we develop a probabilistic model for syntactically analysing such compounds. The model predicts compound noun structures based on knowledge of affinities between nouns, which can be acquired from a corpus. Problems inherent in this corpus-based approach are addressed: data sparseness is overcome by the use of semantically motivated word classes and sense ambiguity is explicitly handled in the model. An implementation based on this model is described in Lauer (1994) and correctly parses 77% of the test set.

BibSonomy :: publication ::

A Probabilistic Model of Compound Nouns

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Abstract

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Endnote record