Implications of an automatic lexical acquisition system

Hastings, Peter

doi:10.1007/3-540-60925-3_52

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…Li (1998) further expands on the subcategorization work by inducing clustering information. Finally, several systems (Knight, 1996;Hastings, 1996;Russell, 1993) learn new words from context, assuming that a large initial lexicon and parsing system are already available.…”

Section: Lexicon Acquisitionmentioning

confidence: 99%

“…Although many others (Sébillot, Bouillon, & Fabre, 2000;Riloff & Jones, 1999;Siskind, 1996;Hastings, 1996;Grefenstette, 1994;Brent, 1991) have presented systems for learning information about lexical semantics, we present here a system for learning lexicons of phrasemeaning pairs. Further, our work is unique in its combination of several features, though prior work has included some of these aspects.…”

Section: Introduction and Overviewmentioning

confidence: 99%

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Acquiring Word-Meaning Mappings for Natural Language Interfaces

Thompson

Mooney

2003

jair

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This paper focuses on a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. Wolfie is part of an integrated system that learns to transform sentences into representations such as logical database queries.Experimental results are presented demonstrating Wolfie's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by Wolfie are compared to those acquired by a similar system, with results favorable to Wolfie. A second set of experiments demonstrates Wolfie's ability to scale to larger and more difficult, albeit artificially generated, corpora.In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance.

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Section: Lexicon Acquisitionmentioning

confidence: 99%

Section: Introduction and Overviewmentioning

confidence: 99%

Acquiring Word-Meaning Mappings for Natural Language Interfaces

Thompson

Mooney

2003

jair

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“…In order to account for the incrementality of the learning process, a new evaluation measure capturing the system's on-line learning accuracy was defined, which is sensitive to taxonomic hierarchies. The results we got were consistently favorable, as our system outperformed those closest in spirit, CAMILLE (Hastings, 1996) and ScIsoR (Rau et al, 1989), by a gain in accuracy on the order of 8%. Also, the system requires relatively few hypothesis spaces (2 to 6 on average) and prunes the concept search space radically, requiring only a few examples (for evaluation details, cf.…”

Section: Hypothesis Rankingmentioning

confidence: 65%

“…Our approach bears a close relationship, however, to the work of Mooney (1987), Berwick (1989), Rau et al (1989), Gomez andSegami (1990), andHastings (1996), who all aim at the automated learning of word meanings from context using a knowledge-intensive approach. But our work differs from theirs in that the need to cope with several competing concept hypotheses and to aim at a reason-based selection in terms of the quality of arguments is not an issue in these studies.…”

Section: Related Workmentioning

confidence: 99%

A text understander that learns

Hahn

Schnattinger

1998

Proceedings of the 36th Annual Meeting on Association for Computational Linguistics -

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We introduce an approach to the automatic acquisition of new concepts fi'om natural language texts which is tightly integrated with the underlying text understanding process. The learning model is centered around the 'quality' of different forms of linguistic and conceptual evidence which underlies the incremental generation and refinement of alternative concept hypotheses, each one capturing a different conceptual reading for an unknown lexical item.

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“…Our approach to text-based concept acquisition bears a close relationship to the work of Gomez and Segami [4], Mooney [10], Reimer [11], and Hastings [7], who all aim at the automated learning of word meanings from context using a knowledge-intensive approach. But our work differs from theirs in that the need to cope with several competing concept hypotheses and to aim at a reason-based selection is not an issue in these studies.…”

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confidence: 99%

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Application Coordination in Pervasive Systems

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Implications of an automatic lexical acquisition system

Cited by 10 publications

References 8 publications

Acquiring Word-Meaning Mappings for Natural Language Interfaces

Acquiring Word-Meaning Mappings for Natural Language Interfaces

A text understander that learns

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