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.
Original language | English |
---|---|
Title of host publication | Artificial intelligence, AI'94 : sowing the seeds for the future |
Subtitle of host publication | proceedings of the 7th Australian Joint Conference on Artificial Intelligence |
Editors | Chengqi Zhang, John Debenham, Dickson Lukose |
Place of Publication | Singapore |
Publisher | World Scientific Publication |
Pages | 474-481 |
Number of pages | 8 |
ISBN (Print) | 9810219202 |
Publication status | Published - 1994 |
Externally published | Yes |
Event | Australian Joint Conference on Artificial Intelligence (7th : 1994) - Armidale, Australia Duration: 21 Nov 1994 → 25 Nov 1994 |
Conference
Conference | Australian Joint Conference on Artificial Intelligence (7th : 1994) |
---|---|
Country/Territory | Australia |
City | Armidale |
Period | 21/11/94 → 25/11/94 |