Seminar on Computational Learning and
Adaptation
Memory-based Learning for Article Generation
Francis Bond
NTT Machine Translation Research Group
Kyoto, Japan
www.kecl.ntt.co.jp/icl/mtg/
Article choice can pose difficult problems in applications such as
machine translation and automated summarization. In this paper, we
investigate the use of corpus data to collect statistical generalizations
about article use in English in order to generate articles automatically
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 generate an article with respect to an
English base noun phrase. We discuss competitive results obtained
using a variety of lexical, syntactic and semantic features that play
an important role in automated article generation.
Minnen, Guido, Francis Bond, and Ann Copestake (2000). Memory-based
Learning for Article Generation. In Proceedings of the Fourth
Conference on Computational Natural Language Learning and of the
Second Learning Language in Logic Workshop (CoNLL-2000 and LLL-2000),
Lisbon. 43-48.
Date: Thurs., Oct 26
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Time: 4:15-5:30PM
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Place: Cordura 100
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