Paper: Memory-Based Learning For Article Generation

ACL ID W00-0708
Title Memory-Based Learning For Article Generation
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2000

Article choice can pose difficult problems in ap- plications such as machine translation and auto- mated summarization. In this paper, we investi- gate the use of corpus data to collect statistical generalizations about article use in English in order to be able to generate articles automati- cally to supplement a symbolic generator. We use data from the Penn Treebank as input to a memory-based learner (TiMBL 3.0; Daelemans et al. , 2000) which predicts whether to gener- ate an article with respect to an English base noun phrase. We discuss competitive results ob- tained using a variety of lexical, syntactic and semantic features that play an important role in automated article generation.