Automatic Acquisition and Efficient Representation of Syntactic Structures

Zach Solan, Eytan Ruppin, David Horn, Shimon Edelman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic structures from corpus data. We extend this principle by applying it recursively, and by using mutual information for estimating category coherence. The resulting model learns, in an unsupervised fashion, highly structured, distributed representations of syntactic knowledge from corpora. It also exhibits promising behavior in tasks usually thought to require representations anchored in a grammar, such as systematicity.

Original languageEnglish
Title of host publicationNIPS 2002
Subtitle of host publicationProceedings of the 15th International Conference on Neural Information Processing Systems
EditorsSuzanna Becker, Sebastian Thrun, Klaus Obermayer
PublisherMIT Press
Pages91-98
Number of pages8
ISBN (Electronic)0262025507, 9780262025508
StatePublished - 2002
Event15th International Conference on Neural Information Processing Systems, NIPS 2002 - Vancouver, Canada
Duration: 9 Dec 200214 Dec 2002

Publication series

NameNIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems

Conference

Conference15th International Conference on Neural Information Processing Systems, NIPS 2002
Country/TerritoryCanada
CityVancouver
Period9/12/0214/12/02

Funding

FundersFunder number
Dan David Prize Foundation
Horowitz Center for Complexity Science
Lillian Lee and Bo Pang
United States-Israel Binational Science Foundation
Tel Aviv University

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