基于冲突评分的孟加拉语关系抽取远程监督减少错误标签

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2020-12-16 DOI:10.1109/CSDE50874.2020.9411604

Tanzim Mahfuz, T. Suha, M. Anwar

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引用次数: 1

摘要

信息提取(IE)的研究领域旨在从非结构化文本数据(如新闻、博客、政府文件等)中提取结构化信息，如实体类型和实体之间的关系。关系提取(RE)处理自由文本中提到的概念之间关系的自动检测。基于知识的远程监督(DS)利用结构化数据对训练语料库进行启发式标注。然而，这种启发式方法会产生一些有噪声的标记数据。在本文中，我们提出了一种使用DS中的冲突评分来减少孟加拉语句子错误标签数量的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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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2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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