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Article

Key semantics extraction by dependency tree mining

Authors:

Satoshi Morinaga,

Hiroki Arimura,

Takahiro Ikeda,

Susumu AkamineAuthors Info & Claims

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Pages 666 - 671

https://doi.org/10.1145/1081870.1081954

Published: 21 August 2005 Publication History

Abstract

We propose a new text mining system which extracts characteristic contents from given documents. We define Key semantics as characteristic sub-structures of syntactic dependencies in the given documents, and consider the following three tasks in this paper: 1)Key semantics extraction: extracting characteristic syntactic dependency structures not only as ordered trees but also as unordered trees and free trees, 2)Redundancy reduction: from the result of extraction, deleting redundant dependency structures such as sub-structures or equivalent structures of the others, and 3)Phrase/sentence reconstruction: generating a phrase or sentence in a natural language corresponding to the extracted structure.Our system is a combination of natural language processing techniques and tree mining techniques. The system consists of the following five units: 1) syntactic dependency analysis unit, 2) input filters, 3) characteristic ordered subtree extraction unit, 4) output filters, and 5) phrase/sentence reconstruction unit. Although ordered trees are extracted in the third unit, the overall behavior of the system can be switched into the extraction of ordered trees, unordered trees, or free trees depending on which of the input filters is/are applied in the second step. The output filters delete redundant trees from the extraction result for efficient knowledge discovery. Finally, phrases or sentences corresponding to the extracted subtrees are reconstructed by utilizing the input documents.We demonstrate the validity of our system by showing experimental results using real data collected at a help desk and TDT pilot corpus.

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Cited By

Xu XCheng ZXiao GZhang YZhang HMeng H(2024)Finite-element analysis case retrieval based on an ontology semantic treeArtificial Intelligence for Engineering Design, Analysis and Manufacturing10.1017/S089006042400004038Online publication date: 14-May-2024
https://doi.org/10.1017/S0890060424000040
Arimura H(2010)Efficient Algorithms for Discovering Frequent and Maximal Substructures from Large Semistructured DataComputer and Information Sciences10.1007/978-90-481-9794-1_66(353-358)Online publication date: 18-Aug-2010
https://doi.org/10.1007/978-90-481-9794-1_66
Arimura H(2008)Efficient algorithms for mining frequent and closed patterns from semi-structured dataProceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining10.5555/1786574.1786578(2-13)Online publication date: 20-May-2008
https://dl.acm.org/doi/10.5555/1786574.1786578
Show More Cited By

Index Terms

Key semantics extraction by dependency tree mining
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

August 2005

844 pages

ISBN:159593135X

DOI:10.1145/1081870

General Chair:
Robert Grossman
University of Illinois at Chicago & Open Data Partners, USA
,
Program Chairs:
Roberto Bayardo
IBM Almaden Research, USA
,
Kristin Bennett
RPI, USA

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 August 2005

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KDD05

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KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2005

Illinois, Chicago, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Cited By

Xu XCheng ZXiao GZhang YZhang HMeng H(2024)Finite-element analysis case retrieval based on an ontology semantic treeArtificial Intelligence for Engineering Design, Analysis and Manufacturing10.1017/S089006042400004038Online publication date: 14-May-2024
https://doi.org/10.1017/S0890060424000040
Arimura H(2010)Efficient Algorithms for Discovering Frequent and Maximal Substructures from Large Semistructured DataComputer and Information Sciences10.1007/978-90-481-9794-1_66(353-358)Online publication date: 18-Aug-2010
https://doi.org/10.1007/978-90-481-9794-1_66
Arimura H(2008)Efficient algorithms for mining frequent and closed patterns from semi-structured dataProceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining10.5555/1786574.1786578(2-13)Online publication date: 20-May-2008
https://dl.acm.org/doi/10.5555/1786574.1786578
Arimura H(2008)Efficient Algorithms for Mining Frequent and Closed Patterns from Semi-structured DataAdvances in Knowledge Discovery and Data Mining10.1007/978-3-540-68125-0_2(2-13)Online publication date: 2008
https://doi.org/10.1007/978-3-540-68125-0_2

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