Work

Learning the countability of English nouns from corpus data

Year: 2003

Type: conference-paper

Abstract: This paper describes a method for learning the countability preferences of English nouns from raw text corpora. The method maps the corpus-attested lexico-syntactic properties of each noun onto a feature vector, and uses a suite of memory-based classifiers to predict membership in 4 countability classes. We were able to assign countability to English nouns with a precision of 94.6%.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.3115/1075096.1075155

Authors: Timothy Baldwin, Francis Bond

Institutions: Stanford University, NTT (Japan)

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: Idiomatic Language: Multiword Expressions