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Relational feature engineering of natural language processing

Authors:

Gunther HeidemannAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 1705 - 1708

https://doi.org/10.1145/1871437.1871709

Published: 26 October 2010 Publication History

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Abstract

We present a new framework for feature engineering of natural language processing that is based on a relational data model of text. It includes fast and flexible methods for implementing and extracting new features and thereby reduces the effort of creating an NLP system for a particular task.

In an instantiation and evaluation of the framework for the problem of coreference resolution in multiple languages, we were able to obtain competitive results in a short implementation period. This demonstrates the potential power of our framework for feature engineering.

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Cited By

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Ślęzak DChądzyńska-Krasowska A(2020)Approximate Decision Tree Induction over Approximately Engineered Data FeaturesRough Sets10.1007/978-3-030-52705-1_28(376-384)Online publication date: 7-Jul-2020
https://doi.org/10.1007/978-3-030-52705-1_28
Qunhui Wu Jianghua Lv Shilong Ma Hao Wang (2015)EET: Efficient event tracking over emergency-oriented web data2015 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2015.7280798(1-8)Online publication date: Jul-2015
https://doi.org/10.1109/IJCNN.2015.7280798
Chen C(2014)FEATURE SELECTION BASED ON COMPACTNESS AND SEPARABILITYComputational Intelligence10.1111/coin.1201030:3(636-656)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1111/coin.12010
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Index Terms

Relational feature engineering of natural language processing
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2010

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CIKM '10

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CIKM '10: International Conference on Information and Knowledge Management

October 26 - 30, 2010

ON, Toronto, Canada

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Ślęzak DChądzyńska-Krasowska A(2020)Approximate Decision Tree Induction over Approximately Engineered Data FeaturesRough Sets10.1007/978-3-030-52705-1_28(376-384)Online publication date: 7-Jul-2020
https://doi.org/10.1007/978-3-030-52705-1_28
Qunhui Wu Jianghua Lv Shilong Ma Hao Wang (2015)EET: Efficient event tracking over emergency-oriented web data2015 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2015.7280798(1-8)Online publication date: Jul-2015
https://doi.org/10.1109/IJCNN.2015.7280798
Chen C(2014)FEATURE SELECTION BASED ON COMPACTNESS AND SEPARABILITYComputational Intelligence10.1111/coin.1201030:3(636-656)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1111/coin.12010
Kowalski MŚlęzak DStencel KPardel PGrzegorowski MKijowski M(2012)RDBMS Model for Scientific Articles AnalyticsIntelligent Tools for Building a Scientific Information Platform10.1007/978-3-642-24809-2_4(49-60)Online publication date: 24-Jan-2012
https://doi.org/10.1007/978-3-642-24809-2_4
Kobdani HSchütze HPradhan S(2011)Supervised coreference resolution with SUCREProceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task10.5555/2132936.2132947(71-75)Online publication date: 23-Jun-2011
https://dl.acm.org/doi/10.5555/2132936.2132947
Burkovski AKessler WHeidemann GKobdani HSchütze H(2011)Self organizing maps in NLPProceedings of the 8th international conference on Advances in self-organizing maps10.5555/2026666.2026694(228-237)Online publication date: 13-Jun-2011
https://dl.acm.org/doi/10.5555/2026666.2026694
Kobdani HSchütze HSchiehlen MKamp HLin D(2011)Bootstrapping coreference resolution using word associationsProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 110.5555/2002472.2002572(783-792)Online publication date: 19-Jun-2011
https://dl.acm.org/doi/10.5555/2002472.2002572
Burkovski AKessler WHeidemann GKobdani HSchütze H(2011)Self Organizing Maps in NLP: Exploration of Coreference Feature SpaceAdvances in Self-Organizing Maps10.1007/978-3-642-21566-7_23(228-237)Online publication date: 2011
https://doi.org/10.1007/978-3-642-21566-7_23

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