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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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  1/2016 - 2

Information Extraction Using Distant Supervision and Semantic Similarities

PARK, Y. See more information about PARK, Y. on SCOPUS See more information about PARK, Y. on IEEExplore See more information about PARK, Y. on Web of Science, KANG, S. See more information about  KANG, S. on SCOPUS See more information about  KANG, S. on SCOPUS See more information about KANG, S. on Web of Science, SEO, J. See more information about SEO, J. on SCOPUS See more information about SEO, J. on SCOPUS See more information about SEO, J. on Web of Science
 
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Download PDF pdficon (1,163 KB) | Citation | Downloads: 370 | Views: 567

Author keywords
relation extraction, unsupervised learning, distant supervision, information extraction, natural language processing

References keywords
extraction(15), link(14), computational(12), relation(11), meeting(10), linguistics(10), association(10), supervision(7), distant(7), semantic(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-02-28
Volume 16, Issue 1, Year 2016, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.01002
Web of Science Accession Number: 000376995400002
SCOPUS ID: 84960113172

Abstract
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Information extraction is one of the main research tasks in natural language processing and text mining that extracts useful information from unstructured sentences. Information extraction techniques include named entity recognition, relation extraction, and co-reference resolution. Among them, relation extraction refers to a task that extracts semantic relations between entities such as personal and geographic names in documents. This is an important research area, which is used in knowledge base construction and question and answering systems. This study presents relation extraction using a distant supervision learning technique among semi-supervised learning methods, which have been spotlighted in recent years to reduce human manual work and costs required for supervised learning. That is, this study proposes a method that can improve relation extraction by improving a distant supervision learning technique by applying a clustering method to create a learning corpus and semantic analysis for relation extraction that is difficult to identify using existing distant supervision. Through comparison experiments of various semantic similarity comparison methods, similarity calculation methods that are useful to relation extraction using distant supervision are searched, and a large number of accurate relation triples can be extracted using the proposed structural advantages and semantic similarity comparison.


References | Cited By  «-- Click to see who has cited this paper

[1] M. Craven and J. Kumlien, "Constructing Biological Knowledge Bases by Extracting Information from Text Sources," in Proc. of International Conference on Intelligent System for Molecular Biology, pp. 77-86, 1999.

[2] K. Bollacker, C. Evans, P. Paritosh, T. Sturge, J. Taylor, "Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge," in Proc. of SIGMOD, pp. 1247-1250, 2008.
[CrossRef] [SCOPUS Times Cited 474]


[3] N. Kambhatla, "Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Extracting Relations," in Proc. of Annual Meeting of the Association for Computational Linguistics, pp. 178-181, 2004.
[CrossRef]


[4] G. Zhou, J. Su, J. Zhang, M. Zhang, "Exploring various knowledge in relation extraction," in Proc. of Annual Meeting of the Association for Computational Linguistics, pp. 427-434, 2005.
[CrossRef]


[5] R. Bunescu, R. Mooney, "A Shortest Path Dependency Kernel for Relation Extraction". in Proc. of HLT/EMNLP, pp. 724-731, 2005.
[CrossRef]


[6] B. Plank, A. Moschitti, "Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction," in Proc. of Annual Meeting of the Association for Computational Linguistics, pp. 1498-1507, 2013. [Online] Available: Temporary on-line reference link removed - see the PDF document

[7] F. Wu, D. Weld, "Open information extraction using Wikipedia," in Proc. of Annual Meeting of the Association for Computational Linguistics, pp. 118-127, 2010. [Online] Available: Temporary on-line reference link removed - see the PDF document

[8] M. Banko, M. Cafarella, S. Soderland, M. Broadhead, O. Etzioni, "Open information extraction from the web," in Proc. of International Joint Conference on Artificial Intelligence, pp. 2670-2676, 2007.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 194]


[9] M. Mintz, S. Bills, R. Snow, D. Jurafsky, "Distant supervision for relation extraction without labeled data," in Proc. of Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 1003-1011, 2009. [Online] Available: Temporary on-line reference link removed - see the PDF document

[10] S. Takamatsu, I. Sato, H. Nakagawa, "Reducing Wrong Labels in Distant Supervision for Relation Extraction," in Proc. of Annual Meeting of the Association for Computational Linguistics, pp. 721-729, 2012. [Online] Available: Temporary on-line reference link removed - see the PDF document

[11] I. Augenstein, "Seed Selection for Distantly Supervised Web-Based Relation Extraction," in Proc. of COLING Workshop on Semantic Web and Information Extraction, pp. 17-24, 2014. [Online] Available: Temporary on-line reference link removed - see the PDF document

[12] X. Zhang, J. Zhang, J. Zeng, J. Yan, Z. Chen, Z. Sui, "Towards Accurate Distant Supervision For Relational Facts Extraction," in Proc. of Annual Meeting of the Association for Computational Linguistics, pp. 810-815, 2013. [Online] Available: Temporary on-line reference link removed - see the PDF document

[13] M. Surdeanu, J. Tibshirani, R. Nallapati, C. Manning, "Multi-instance Multi-label Learning for Relation Extraction," in Proc. of Empirical Methods in Natural Language Processing and Computational Natural Language Learning. pp. 455-465, 2012. [Online] Available: Temporary on-line reference link removed - see the PDF document

[14] M. Fan, D. Zhao, Q. Zhou, Z. Liu, T. Zheng, E. Chang, "Distant Supervision for Relation Extraction with Matrix Completion", in Proc. of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 839-849, 2014. [Online] Available: Temporary on-line reference link removed - see the PDF document

[15] T. Nguyen, A. Moschitti, "End-to-end Relation Extraction using Distant Supervision from External Semantic Repositories," in Proc. of Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 277-282, 2014. [Online] Available: Temporary on-line reference link removed - see the PDF document

[16] S. Krause, H. Li, H. Uszkoreit, F, Xu, "Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web," in Proc. of International Semantic Web Conference, pp. 263-278, 2012. [Online] Available: Temporary on-line reference link removed - see the PDF document

[17] G. Garrido, A. Penas, B. Cabaleiro, A. Rodrigo, "Temporally Anchored Relation Extraction," in Proc. of Annual Meeting of the Association for Computational Linguistics, pp. 107-116, 2012. [Online] Available: Temporary on-line reference link removed - see the PDF document

[18] M. Surdeanu, D. McClosky, J. Tibshirani, J. Bauer, A. Chang, V. Spitkovsky, C. Manning, "A Simple Distant Supervision Approach for the TAC-KBP Slot Filling Task," in Proc. of Text Analysis Conference, 2010. [Online] Available: Temporary on-line reference link removed - see the PDF document

[19] Y, Kim. "Automatic Training Corpus Generation Method of Named Entity Recognition using Big Data", Ms. Thesis, Sogang University, 2014. [Online] Available: Temporary on-line reference link removed - see the PDF document

[20] D. Lin. "Extracting Collocations from Text Corpora," Workshop on Computational Terminology, pp. 57-63. 1998. [Online] Available: Temporary on-line reference link removed - see the PDF document



References Weight

Web of Science® Citations for all references: 67 TCR
SCOPUS® Citations for all references: 668 TCR

Web of Science® Average Citations per reference: 3 ACR
SCOPUS® Average Citations per reference: 32 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2016-12-06 23:10 in 29 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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