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.

References

[1]

T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, and S. Arikawa: Efficient Substructure Discovery from Large Semi-structured Data, Proc. SDM'02, pp:158--174, SIAM, 2002.]]

[2]

T. Asai, H. Arimura, T. Uno, S. Nakano: Discovering Frequent Substructures in Large Unordered Trees, Proc. DS'03, LNAI 2843, Springer-Verlag, pp:47--61, 2003.]]

[3]

Y. Chi, Y. Yang, and R. R. Muntz: Indexing and mining free trees, Proc. IEEE ICDM'03, pp:509--512, 2003.]]

Digital Library

[4]

A. Inokuchi, T. Washio, H. Motoda: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data, Proc. PKDD 2000, LNAI 1910, Springer-Verlag, pp:13--23, 2000.]]

Digital Library

[5]

D. Klein and C.D. Manning: Fast Exact Inference with a Factored Model for Natural Language Parsing, Proc. NIPS 2002, pp:3--10, 2002.]]

[6]

T. Kudo and Y. Matsumoto: A Boosting Algorithm for Classification of Semi-Structured Text, Proc. of EMNLP, pp:301--308, 2004.]]

[7]

M. Kuramochi, G. Karypis: Frequent Subgraph Discovery, Proc. IEEE ICDM'01, pp:313--320, 2001.]]

Digital Library

[8]

S. Morinaga and K. Yamanishi: Tracking Dynamics of Topic Trends Using a Finite Mixture Model, Proc. KDD2004, pp:811--816, 2004.]]

Digital Library

[9]

S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fukushima: Mining Product Reputations on the Web, Proc. KDD2002, pp:341--349, 2002.]]

Digital Library

[10]

S. Nakano: Efficient generation of plane trees, IPL, vol. 84, pp:167--172, 2002.]]

Digital Library

[11]

S. Nijssen, J. N. Kok: Effcient Discovery of Frequent Unordered Trees, Proc. MGTS'03, pp:55--64, Sep. 2003.]]

[12]

J. Pei, J. Han: Constrained frequent pattern mining: a pattern-growth view, SIGKDD Explorations 4(1), pp:31--39, 2002.]]

Digital Library

[13]

Y. Sakao, T. Ikeda, K. Satoh, and S. Akamine: Japanese Language Analysis for Syntactic Tree Mining to Extract Characteristic Contents, Proc. of the 11th Annual Meeting of the Association for NLP, 2005. (in Japanese).]]

[14]

K. Satoh, T. Ikeda, T. Nakata, and S. Osada: Japanese Processing Middleware for Customer Relationship Management, in Proc. of the 9th Annual Meeting of the Association for NLP, pp.109--112, 2003. (in Japanese).]]

[15]

http://www-nlp.stanford.edu/software/lex-parser.shtml]]

[16]

http://www.nist.gov/speech/tests/tdt/]]

[17]

http://www.topicscope.com/ (in Japanese).]]

[18]

K. Yamanishi: A decision-theoretic extension of stochastic complexity and its applications to learning, IEEE Trans. on Information Theory, vol. 44(4), pp:1424--1439, 1998.]]

Digital Library

[19]

K. Yamanishi and H. Li: Mining open answers in questionnaire data, IEEE Intelligent Systems, Sept/Oct, pp:58--63, 2002.]]

Digital Library

[20]

X. Yan, J. Han: gSpan: Graph-Based Substructure Pattern Mining, Proc. IEEE ICDM'02, pp:721--724, 2002.]]

Digital Library

[21]

M. J. Zaki: Efficiently Mining Frequent Trees in a Forest, Proc. KDD2002, pp:71--80, 2002.]]

Digital Library

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

Recommendations

Phrase dependency machine translation with quasi-synchronous tree-to-tree features

Recent research has shown clear improvement in translation quality by exploiting linguistic syntax for either the source or target language. However, when using syntax for both languages "tree-to-tree" translation, there is evidence that syntactic ...
Stochastic Data-to-Text Generation Using Syntactic Dependency Information
Abstract
Data-to-Text Generation (D2T) is one of the most important sub-fields of Natural Language Generation where structured data is transcribed into natural language text. Several solutions have been proposed for D2T so far with relative ...
Semantic relation extraction with kernels over typed dependency trees
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

An important step for understanding the semantic content of text is the extraction of semantic relations between entities in natural language documents. Automatic extraction techniques have to be able to identify different versions of the same relation ...

Comments

Information & Contributors

Information

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 August 2005

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

KDD05

Sponsor:

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

August 21 - 24, 2005

Illinois, Chicago, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
758
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

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

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents