research-article

Automatic rule refinement for information extraction

Editors: Elisa Bertino, Paolo Atzeni, Kian Lee Tan, Yi Chen, Y. C. Tay Authors:

Laura Chiticariu,

H. V. Jagadish,

Frederick R. ReissAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 3, Issue 1-2

Pages 588 - 597

https://doi.org/10.14778/1920841.1920916

Published: 01 September 2010 Publication History

Abstract

Rule-based information extraction from text is increasingly being used to populate databases and to support structured queries on unstructured text. Specification of suitable information extraction rules requires considerable skill and standard practice is to refine rules iteratively, with substantial effort. In this paper, we show that techniques developed in the context of data provenance, to determine the lineage of a tuple in a database, can be leveraged to assist in rule refinement. Specifically, given a set of extraction rules and correct and incorrect extracted data, we have developed a technique to suggest a ranked list of rule modifications that an expert rule specifier can consider. We implemented our technique in the SystemT information extraction system developed at IBM Research -- Almaden and experimentally demonstrate its effectiveness.

References

[1]

Database languages -- SQL -- Part 1: Framework (SQL/Framework). Technical report. ISO/IEC 9075--1:2003.

[2]

The Enron corpus. www.cs.cmu.edu/enron/.

[3]

Automatic Content Extraction 2005 Evaluation Dataset, 2005.

[4]

E. Agichtein and L. Gravano. Snowball: Extracting Relations from Large Plain-Text Collections. In ACM DL, 2000.

Digital Library

[5]

D. E. Appelt and B. Onyshkevych. The Common Pattern Specification Language. In TIPSTER workshop, 1998.

Digital Library

[6]

N. Ashish, S. Mehrotra, and P. Pirzadeh. XAR: An Integrated Framework for Information Extraction. In WRI Wold Congress on Computer Science and Information Engineering, 2009.

Digital Library

[7]

J. L. Bentley. Programming Pearls: Algorithm Design Techniques. Commun. ACM, 27(9):865--873, 1984.

Digital Library

[8]

B. Boguraev. Annotation-based Finite State Processing in a Large-Scale NLP Architecture. In RANLP, 2003.

[9]

A. Chapman and H. V. Jagadish. Why Not? In SIGMOD, 2009.

Digital Library

[10]

J. Cheney, L. Chiticariu, and W. Tan. Provenance in Databases: Why, How, and Where. Foundations and Trends in Databases, 1(4):379--474, 2009.

Digital Library

[11]

L. Chiticariu, R. Krishnamurthy, Y. Li, S. Raghavan, F. Reiss, and S. Vaithyanathan. SystemT: An Algebraic Approach to Declarative Information Extraction. In ACL, 2010.

Digital Library

[12]

H. Cunningham. JAPE: a Java Annotation Patterns Engine. Research Memorandum CS - 99 - 06, University of Sheffield, May 1999.

[13]

A. Das Sarma, A. Jain, and D. Srivastava. I4E: Interactive Investigation of Iterative Information Extraction. In SIGMOD, 2010.

Digital Library

[14]

D. DeJong. An Overview of the FRUMP System. In Strategies for Natural language Processing. 1982.

[15]

D. Freitag. Multistrategy Learning for Information Extraction. In ICML, 1998.

Digital Library

[16]

B. Glavic and G. Alonso. Perm: Processing Provenance and Data on the Same Data Model through Query Rewriting. In ICDE, 2009.

Digital Library

[17]

T. J. Green, G. Karvounarakis, and V. Tannen. Provenance Semirings. In PODS, 2007.

Digital Library

[18]

M. Herschel and M. Hernandez. Explaining Missing Answers to SPJUA Queries. PVLDB, 2010.

Digital Library

[19]

J. R. Hobbs, D. Appelt, J. Bear, D. Israel, M. Kameyama, and M. Tyson. FASTUS: a System for Extracting Information from Text. In HLT, 1993.

Digital Library

[20]

J. Huang, T. Chen, A. Doan, and J. F. Naughton. On the Provenance of Non-Answers to Queries over Extracted Data. PVLDB, 1(1), 2008.

Digital Library

[21]

A. Jain, P. Ipeirotis, A. Doan, and L. Gravano. Join Optimization of Information Extraction Output: Quality Matters! In ICDE, 2009.

Digital Library

[22]

R. Krishnamurthy, Y. Li, S. Raghavan, F. Reiss, S. Vaithyanathan, and H. Zhu. SystemT: a System for Declarative Information Extraction. SIGMOD Record, 37(4):7--13, 2008.

Digital Library

[23]

J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In ICML, 2001.

Digital Library

[24]

W. Lehnert, J. McCarthy, S. Soderland, E. Riloff, C. Cardie, J. Peterson, F. Feng, C. Dolan, and S. Goldman. UMass/Hughes: Description of the CIRCUS System Used for MUC-5. In MUC, 1993.

Digital Library

[25]

Y. Li, R. Krishnamurthy, S. Raghavan, S. Vaithyanathan, and H. V. Jagadish. Regular Expression Learning for Information Extraction. In EMNLP, 2008.

Digital Library

[26]

F. Peng and A. McCallum. Accurate Information Extraction from Research Papers Using Conditional Random Fields. In HLT-NAACL, 2004.

[27]

F. Reiss, S. Raghavan, R. Krishnamurthy, H. Zhu, and S. Vaithyanathan. An Algebraic Approach to Rule-Based Information Extraction. In ICDE, 2008.

Digital Library

[28]

E. Riloff. Automatically Constructing a Dictionary for Information Extraction Tasks. In KDD, 1993.

[29]

W. Shen, P. DeRose, R. McCann, A. Doan, and R. Ramakrishnan. Toward Best-Effort Information Extraction. In SIGMOD, 2008.

Digital Library

[30]

W. Shen, A. Doan, J. F. Naughton, and R. Ramakrishnan. Declarative Information Extraction Using Datalog with Embedded Extraction Predicates. In VLDB, 2007.

Digital Library

[31]

S. G. Soderland. Learning Text Analysis Rules for Domain-specific Natural Language Processing. Technical report, U. Mass., 1996.

Digital Library

[32]

S. Tata, J. M. Patel, J. S. Friedman, and A. Swaroop. Declarative Querying for Biological Sequences. In ICDE, 2006.

Digital Library

[33]

C. Thompson, M. Califf, and R. Mooney. Active Learning for Natural Language Parsing and Information Extraction. In ICML, 1999.

Digital Library

[34]

E. F. Tjong Kim Sang and F. De Meulder. Introduction to the CoNLL-2003 Shared Task: Language-independent Named Entity Recognition. In CoNLL at HLT-NAACL, 2003.

Digital Library

[35]

A. Yates, M. Banko, M.Broadhead, M. J. Cafarella, O. Etzioni, and S. Soderland. TextRunner: Open Information Extraction on the Web. In HLT-NAACL (Demonstration), 2007.

Digital Library

[36]

S. Zhao and R. Grishman. Extracting Relations with Integrated Information Using Kernel Methods. In ACL, 2005.

Digital Library

Cited By

Tang NFan JLi FTu JDu XLi GMadden SOuzzani M(2021)RPTProceedings of the VLDB Endowment10.14778/3457390.345739114:8(1254-1261)Online publication date: 21-Oct-2021
https://dl.acm.org/doi/10.14778/3457390.3457391
Kassaie BTompa FBorghoff USchimmler S(2019)Predictable and Consistent Information ExtractionProceedings of the ACM Symposium on Document Engineering 201910.1145/3342558.3345391(1-10)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3342558.3345391
Freydenberger DKimelfeld BPeterfreund LVan den Bussche JArenas M(2018)Joining Extractions of Regular ExpressionsProceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3196959.3196967(137-149)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3196959.3196967
Show More Cited By

Automatic rule refinement for information extraction
1. Information systems
  1. Data management systems

Recommendations

Applying Rule Extraction & Rule Refinement techniques to (Blackbox) Classifiers
K-CAP '15: Proceedings of the 8th International Conference on Knowledge Capture

Black-box classifiers are able to classify unseen instances, once they have been trained on an appropriate (domain) dataset. Such classifiers have the advantage of being generally very efficient but the disadvantage of not being able to explain their ...
Refinement of rule sets with JoJo
ECML'93: Proceedings of the 6th European Conference on Machine Learning

In the paper we discuss a new approach for learning classification rules from examples. We sketch out the algorithm JoJo and its extension to a four step procedure which can be used to incrementally refine a set of classification rules. Incorrect rules ...
Uncertainty management in rule-based information extraction systems
SIGMOD '09: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data

Rule-based information extraction is a process by which structured objects are extracted from text based on user-defined rules. The compositional nature of rule-based information extraction also allows rules to be expressed over previously extracted ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 3, Issue 1-2

September 2010

1658 pages

ISSN:2150-8097

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 September 2010

Published in PVLDB Volume 3, Issue 1-2

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
387
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 06 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tang NFan JLi FTu JDu XLi GMadden SOuzzani M(2021)RPTProceedings of the VLDB Endowment10.14778/3457390.345739114:8(1254-1261)Online publication date: 21-Oct-2021
https://dl.acm.org/doi/10.14778/3457390.3457391
Kassaie BTompa FBorghoff USchimmler S(2019)Predictable and Consistent Information ExtractionProceedings of the ACM Symposium on Document Engineering 201910.1145/3342558.3345391(1-10)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3342558.3345391
Freydenberger DKimelfeld BPeterfreund LVan den Bussche JArenas M(2018)Joining Extractions of Regular ExpressionsProceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3196959.3196967(137-149)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3196959.3196967
Hanafi MAbouzied AChiticariu LLi YChirkova RYang JSuciu D(2017)Synthesizing Extraction Rules from User Examples with SEERProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3056443(1687-1690)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3056443
Christen PGayler RTran KFisher JVatsalan D(2016)Automatic Discovery of Abnormal Values in Large Textual DatabasesJournal of Data and Information Quality10.1145/28893117:1-2(1-31)Online publication date: 19-Apr-2016
https://dl.acm.org/doi/10.1145/2889311
Fagin RKimelfeld BReiss FVansummeren S(2016)Declarative Cleaning of Inconsistencies in Information ExtractionACM Transactions on Database Systems10.1145/287720241:1(1-44)Online publication date: 7-Apr-2016
https://dl.acm.org/doi/10.1145/2877202
Fagin RKimelfeld BReiss FVansummeren S(2015)Document SpannersJournal of the ACM10.1145/269944262:2(1-51)Online publication date: 6-May-2015
https://dl.acm.org/doi/10.1145/2699442
Kimelfeld BHull RGrohe M(2014)Database principles in information extractionProceedings of the 33rd ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems10.1145/2594538.2594563(156-163)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2594538.2594563
Fagin RKimelfeld BReiss FVansummeren SHull RGrohe M(2014)Cleaning inconsistencies in information extraction via prioritized repairsProceedings of the 33rd ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems10.1145/2594538.2594540(164-175)Online publication date: 18-Jun-2014
https://dl.acm.org/doi/10.1145/2594538.2594540
Islam MLiu CZhou R(2014)FlexIQJournal of Systems and Software10.1016/j.jss.2014.07.01197:C(97-117)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.jss.2014.07.011
Show More Cited By

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents