Collective entity linking on relational graph model with mentions

Jing Gong; Chong Feng; Yong Liu; Ge Shi; Heyan Huang

doi:10.1007/978-3-319-69005-6_14

Collective entity linking on relational graph model with mentions

Jing Gong, Chong Feng^*, Yong Liu, Ge Shi, Heyan Huang

^*Corresponding author for this work

School of Computer Science and Technology

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

3 Citations (Scopus)

Abstract

Given a source document with extracted mentions, entity linking calls for mapping the mention to an entity in reference knowledge base. Previous entity linking approaches mainly focus on generic statistic features to link mentions independently. However, additional interdependence among mentions in the same document achieved from relational analysis can improve the accuracy. This paper propose a collective entity linking model which effectively leverages the global interdependence among mentions in the same source document. The model unifies semantic relations and co-reference relations into relational inference for semantic information extraction. Graph based linking algorithm is utilized to ensure per mention with only one candidate entity. Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.

Original language	English
Title of host publication	Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings
Editors	Maosong Sun, Baobao Chang, Xiaojie Wang, Deyi Xiong
Publisher	Springer Verlag
Pages	159-171
Number of pages	13
ISBN (Print)	9783319690049
DOIs	https://doi.org/10.1007/978-3-319-69005-6_14
Publication status	Published - 2017
Event	16th China National Conference on Computational Linguistics, CCL 2017 and 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017 - Nanjing, China Duration: 13 Oct 2017 → 15 Oct 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10565 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th China National Conference on Computational Linguistics, CCL 2017 and 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017
Country/Territory	China
City	Nanjing
Period	13/10/17 → 15/10/17

Keywords

Collective entity linking
Entity disambiguation
Relational graph

Access to Document

10.1007/978-3-319-69005-6_14

Cite this

Gong, J., Feng, C., Liu, Y., Shi, G., & Huang, H. (2017). Collective entity linking on relational graph model with mentions. In M. Sun, B. Chang, X. Wang, & D. Xiong (Eds.), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings (pp. 159-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10565 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-69005-6_14

Gong, Jing ; Feng, Chong ; Liu, Yong et al. / Collective entity linking on relational graph model with mentions. Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings. editor / Maosong Sun ; Baobao Chang ; Xiaojie Wang ; Deyi Xiong. Springer Verlag, 2017. pp. 159-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f25796d8d61b4fc59996941f25c4adb1,

title = "Collective entity linking on relational graph model with mentions",

abstract = "Given a source document with extracted mentions, entity linking calls for mapping the mention to an entity in reference knowledge base. Previous entity linking approaches mainly focus on generic statistic features to link mentions independently. However, additional interdependence among mentions in the same document achieved from relational analysis can improve the accuracy. This paper propose a collective entity linking model which effectively leverages the global interdependence among mentions in the same source document. The model unifies semantic relations and co-reference relations into relational inference for semantic information extraction. Graph based linking algorithm is utilized to ensure per mention with only one candidate entity. Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.",

keywords = "Collective entity linking, Entity disambiguation, Relational graph",

author = "Jing Gong and Chong Feng and Yong Liu and Ge Shi and Heyan Huang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th China National Conference on Computational Linguistics, CCL 2017 and 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017 ; Conference date: 13-10-2017 Through 15-10-2017",

year = "2017",

doi = "10.1007/978-3-319-69005-6_14",

language = "English",

isbn = "9783319690049",

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

publisher = "Springer Verlag",

pages = "159--171",

editor = "Maosong Sun and Baobao Chang and Xiaojie Wang and Deyi Xiong",

booktitle = "Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings",

address = "Germany",

}

Gong, J, Feng, C, Liu, Y, Shi, G & Huang, H 2017, Collective entity linking on relational graph model with mentions. in M Sun, B Chang, X Wang & D Xiong (eds), Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10565 LNAI, Springer Verlag, pp. 159-171, 16th China National Conference on Computational Linguistics, CCL 2017 and 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017, Nanjing, China, 13/10/17. https://doi.org/10.1007/978-3-319-69005-6_14

Collective entity linking on relational graph model with mentions. / Gong, Jing; Feng, Chong; Liu, Yong et al.
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings. ed. / Maosong Sun; Baobao Chang; Xiaojie Wang; Deyi Xiong. Springer Verlag, 2017. p. 159-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10565 LNAI).

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

TY - GEN

T1 - Collective entity linking on relational graph model with mentions

AU - Gong, Jing

AU - Feng, Chong

AU - Liu, Yong

AU - Shi, Ge

AU - Huang, Heyan

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Given a source document with extracted mentions, entity linking calls for mapping the mention to an entity in reference knowledge base. Previous entity linking approaches mainly focus on generic statistic features to link mentions independently. However, additional interdependence among mentions in the same document achieved from relational analysis can improve the accuracy. This paper propose a collective entity linking model which effectively leverages the global interdependence among mentions in the same source document. The model unifies semantic relations and co-reference relations into relational inference for semantic information extraction. Graph based linking algorithm is utilized to ensure per mention with only one candidate entity. Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.

AB - Given a source document with extracted mentions, entity linking calls for mapping the mention to an entity in reference knowledge base. Previous entity linking approaches mainly focus on generic statistic features to link mentions independently. However, additional interdependence among mentions in the same document achieved from relational analysis can improve the accuracy. This paper propose a collective entity linking model which effectively leverages the global interdependence among mentions in the same source document. The model unifies semantic relations and co-reference relations into relational inference for semantic information extraction. Graph based linking algorithm is utilized to ensure per mention with only one candidate entity. Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.

KW - Collective entity linking

KW - Entity disambiguation

KW - Relational graph

UR - http://www.scopus.com/inward/record.url?scp=85031424017&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-69005-6_14

DO - 10.1007/978-3-319-69005-6_14

M3 - Conference contribution

AN - SCOPUS:85031424017

SN - 9783319690049

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

SP - 159

EP - 171

BT - Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings

A2 - Sun, Maosong

A2 - Chang, Baobao

A2 - Wang, Xiaojie

A2 - Xiong, Deyi

PB - Springer Verlag

T2 - 16th China National Conference on Computational Linguistics, CCL 2017 and 5th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2017

Y2 - 13 October 2017 through 15 October 2017

ER -

Gong J, Feng C, Liu Y, Shi G, Huang H. Collective entity linking on relational graph model with mentions. In Sun M, Chang B, Wang X, Xiong D, editors, Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 16th China National Conference, CCL 2017 and 5th International Symposium, NLP-NABD 2017, Proceedings. Springer Verlag. 2017. p. 159-171. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-69005-6_14

Collective entity linking on relational graph model with mentions

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