Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language

Tanzim Mahfuz; Tasneem Farhana Suha; Md Musfique Anwar

doi:10.1109/CSDE50874.2020.9411604

Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language

Tanzim Mahfuz
, Tasneem Farhana Suha
, Md Musfique Anwar

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

3 Scopus citations

Abstract

The research area of information extraction (IE) aims to extract structured information such as types of entities and relations between them, from unstructured textual data like newswires, blogs, governmental documents etc. Relation extraction (RE) deals with the automatic detection of relationships between concepts mentioned in free texts. Knowledge-based distant supervision (DS) uses structured data to heuristically label a training corpus. However, this heuristic can generate some noisy labeled data. In this paper, we propose a method using conflict score in DS to reduce the number of wrong labels for Bangla sentences.

Original language	English
Title of host publication	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665419741
DOIs	https://doi.org/10.1109/CSDE50874.2020.9411604
State	Published - 16 Dec 2020
Externally published	Yes
Event	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 - Gold Coast, Australia Duration: 16 Dec 2020 → 18 Dec 2020

Publication series

Name	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020

Conference

Conference	2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
Country/Territory	Australia
City	Gold Coast
Period	16/12/20 → 18/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Conflict score
Distant supervision
Knowledge base
Noisy pattern
Relation extraction

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems and Management
Health Informatics
Communication

Access to Document

10.1109/CSDE50874.2020.9411604

Cite this

Mahfuz, T., Suha, T. F., & Anwar, M. M. (2020). Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 Article 9411604 (2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSDE50874.2020.9411604

Mahfuz, Tanzim ; Suha, Tasneem Farhana ; Anwar, Md Musfique. / Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020).

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title = "Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language",

abstract = "The research area of information extraction (IE) aims to extract structured information such as types of entities and relations between them, from unstructured textual data like newswires, blogs, governmental documents etc. Relation extraction (RE) deals with the automatic detection of relationships between concepts mentioned in free texts. Knowledge-based distant supervision (DS) uses structured data to heuristically label a training corpus. However, this heuristic can generate some noisy labeled data. In this paper, we propose a method using conflict score in DS to reduce the number of wrong labels for Bangla sentences.",

keywords = "Conflict score, Distant supervision, Knowledge base, Noisy pattern, Relation extraction",

author = "Tanzim Mahfuz and Suha, \{Tasneem Farhana\} and Anwar, \{Md Musfique\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020 ; Conference date: 16-12-2020 Through 18-12-2020",

year = "2020",

month = dec,

day = "16",

doi = "10.1109/CSDE50874.2020.9411604",

language = "English",

series = "2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020",

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Mahfuz, T, Suha, TF & Anwar, MM 2020, Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language. in 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020., 9411604, 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020, Gold Coast, Australia, 16/12/20. https://doi.org/10.1109/CSDE50874.2020.9411604

Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language. / Mahfuz, Tanzim; Suha, Tasneem Farhana; Anwar, Md Musfique.
2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9411604 (2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020).

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

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AB - The research area of information extraction (IE) aims to extract structured information such as types of entities and relations between them, from unstructured textual data like newswires, blogs, governmental documents etc. Relation extraction (RE) deals with the automatic detection of relationships between concepts mentioned in free texts. Knowledge-based distant supervision (DS) uses structured data to heuristically label a training corpus. However, this heuristic can generate some noisy labeled data. In this paper, we propose a method using conflict score in DS to reduce the number of wrong labels for Bangla sentences.

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KW - Knowledge base

KW - Noisy pattern

KW - Relation extraction

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M3 - Conference contribution

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BT - 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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Mahfuz T, Suha TF, Anwar MM. Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9411604. (2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020). doi: 10.1109/CSDE50874.2020.9411604

Reducing Wrong Labels using Conflict Score in Distant Supervision for Relation Extraction in Bangla Language

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this