Using graph based method to improve bootstrapping relation extraction

Haibo Li; Danushka Bollegala; Yutaka Matsuo; Mitsuru Ishizuka

doi:10.1007/978-3-642-19437-5_10

Using graph based method to improve bootstrapping relation extraction

Haibo Li^*, Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Many bootstrapping relation extraction systems processing large corpus or working on the Web have been proposed in the literature. These systems usually return a large amount of extracted relationship instances as an out-of-ordered set. However, the returned result set often contains many irrelevant or weakly related instances. Ordering the extracted examples by their relevance to the given seeds is helpful to filter out irrelevant instances. Furthermore, ranking the extracted examples makes the selection of most similar instance easier. In this paper, we use a graph based method to rank the returned relation instances of a bootstrapping relation extraction system. We compare the used algorithm to the existing methods, relevant score based methods and frequency based methods, the results indicate that the proposed algorithm can improve the performance of the bootstrapping relation extraction systems.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	127-138
Number of pages	12
Volume	6609 LNCS
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-19437-5_10
Publication status	Published - 2011
Externally published	Yes
Event	12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011 - Tokyo Duration: 2011 Feb 20 → 2011 Feb 26

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6609 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011
City	Tokyo
Period	11/2/20 → 11/2/26

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Access to Document

10.1007/978-3-642-19437-5_10

Cite this

Li, H., Bollegala, D., Matsuo, Y., & Ishizuka, M. (2011). Using graph based method to improve bootstrapping relation extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6609 LNCS, pp. 127-138). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6609 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-19437-5_10

Using graph based method to improve bootstrapping relation extraction. / Li, Haibo; Bollegala, Danushka; Matsuo, Yutaka et al.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6609 LNCS PART 2. ed. 2011. p. 127-138 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6609 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, H, Bollegala, D, Matsuo, Y & Ishizuka, M 2011, Using graph based method to improve bootstrapping relation extraction. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 6609 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6609 LNCS, pp. 127-138, 12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011, Tokyo, 11/2/20. https://doi.org/10.1007/978-3-642-19437-5_10

Li H, Bollegala D, Matsuo Y, Ishizuka M. Using graph based method to improve bootstrapping relation extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 6609 LNCS. 2011. p. 127-138. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-19437-5_10

Li, Haibo ; Bollegala, Danushka ; Matsuo, Yutaka et al. / Using graph based method to improve bootstrapping relation extraction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6609 LNCS PART 2. ed. 2011. pp. 127-138 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{da73df937774497786c2f691842695a4,

title = "Using graph based method to improve bootstrapping relation extraction",

abstract = "Many bootstrapping relation extraction systems processing large corpus or working on the Web have been proposed in the literature. These systems usually return a large amount of extracted relationship instances as an out-of-ordered set. However, the returned result set often contains many irrelevant or weakly related instances. Ordering the extracted examples by their relevance to the given seeds is helpful to filter out irrelevant instances. Furthermore, ranking the extracted examples makes the selection of most similar instance easier. In this paper, we use a graph based method to rank the returned relation instances of a bootstrapping relation extraction system. We compare the used algorithm to the existing methods, relevant score based methods and frequency based methods, the results indicate that the proposed algorithm can improve the performance of the bootstrapping relation extraction systems.",

author = "Haibo Li and Danushka Bollegala and Yutaka Matsuo and Mitsuru Ishizuka",

year = "2011",

doi = "10.1007/978-3-642-19437-5_10",

language = "English",

isbn = "9783642194368",

volume = "6609 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "127--138",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

edition = "PART 2",

note = "12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011 ; Conference date: 20-02-2011 Through 26-02-2011",

}

TY - GEN

T1 - Using graph based method to improve bootstrapping relation extraction

AU - Li, Haibo

AU - Bollegala, Danushka

AU - Matsuo, Yutaka

AU - Ishizuka, Mitsuru

PY - 2011

Y1 - 2011

N2 - Many bootstrapping relation extraction systems processing large corpus or working on the Web have been proposed in the literature. These systems usually return a large amount of extracted relationship instances as an out-of-ordered set. However, the returned result set often contains many irrelevant or weakly related instances. Ordering the extracted examples by their relevance to the given seeds is helpful to filter out irrelevant instances. Furthermore, ranking the extracted examples makes the selection of most similar instance easier. In this paper, we use a graph based method to rank the returned relation instances of a bootstrapping relation extraction system. We compare the used algorithm to the existing methods, relevant score based methods and frequency based methods, the results indicate that the proposed algorithm can improve the performance of the bootstrapping relation extraction systems.

AB - Many bootstrapping relation extraction systems processing large corpus or working on the Web have been proposed in the literature. These systems usually return a large amount of extracted relationship instances as an out-of-ordered set. However, the returned result set often contains many irrelevant or weakly related instances. Ordering the extracted examples by their relevance to the given seeds is helpful to filter out irrelevant instances. Furthermore, ranking the extracted examples makes the selection of most similar instance easier. In this paper, we use a graph based method to rank the returned relation instances of a bootstrapping relation extraction system. We compare the used algorithm to the existing methods, relevant score based methods and frequency based methods, the results indicate that the proposed algorithm can improve the performance of the bootstrapping relation extraction systems.

UR - http://www.scopus.com/inward/record.url?scp=79952271503&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952271503&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-19437-5_10

DO - 10.1007/978-3-642-19437-5_10

M3 - Conference contribution

AN - SCOPUS:79952271503

SN - 9783642194368

VL - 6609 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 127

EP - 138

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 12th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2011

Y2 - 20 February 2011 through 26 February 2011

ER -

Using graph based method to improve bootstrapping relation extraction

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this