Reducing Human Effort in Named Entity Corpus Construction Based on Ensemble Learning and Annotation Categorization

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原文链接

NETL
Springer Nature

外文摘要：Annotated named entity corpora play a significant role in many natural language processing applications。 However， annotation by humans is time-consuming and costly。 In this paper， we propose a high recall pre-annotator which combines multiple existing named entity taggers based on ensemble learning， to reduce the number of annotations that humans have to add。 In addition， annotations are categorized into normal annotations and candidate annotations based on their estimated confidence， to reduce the number of human corrective actions as well as the total annotation time。 The experiment results show that our approach outperforms the baseline methods in reduction of annotation time without loss in annotation performance (in terms of F-measure)。

外文关键词：

Corpus constructionNamed Entity RecognitionAssisted annotationEnsemble learning

作者：

Tingming Lu、Man Zhu、Zhiqiang Gao

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作者单位：

Key Lab of Computer Network and Information Integration, (Southeast University), Ministry of Education, Nanjing, China,School of Computer Science and Engineering, Southeast University, Nanjing, China

School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

会议名称：

International conference on computer processing of oriental languages;CCF conference on natural language processing and Chinese computing

会议地点：

Kunming(CN)

会议母体文献：

Natural language understanding and intelligent applications

页码：

263-274

出版时间：

2016

DOI：

10.1007/978-3-319-50496-4_22