A Probabilistic model of compound nouns

Mark Dras, Mark Lauer

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution


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.
Original languageEnglish
Title of host publicationArtificial intelligence, AI'94 : sowing the seeds for the future
Subtitle of host publicationproceedings of the 7th Australian Joint Conference on Artificial Intelligence
EditorsChengqi Zhang, John Debenham, Dickson Lukose
Place of PublicationSingapore
PublisherWorld Scientific Publication
Number of pages8
ISBN (Print)9810219202
Publication statusPublished - 1994
Externally publishedYes
EventAustralian Joint Conference on Artificial Intelligence (7th : 1994) - Armidale, Australia
Duration: 21 Nov 199425 Nov 1994


ConferenceAustralian Joint Conference on Artificial Intelligence (7th : 1994)

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