属性抽取的主要目标是从非结构化文本中获取实体的属性值。为了从文本中抽取出人物属性,通常需要大量的标注数据,然而这些数据资源却十分稀少。为了解决这个问题,该文从百科类网页的表格数据出发,构建了人物属性表,然后采用远程监督的方法得到大规模、多类别的人物属性标注语料,从而免去了人工标注的繁琐流程。针对新构建的数据集,分别使用条件随机场(CRF)和双向长短期记忆-条件随机场(BiLSTM-CRF)构建了属性抽取的两个基线模型。实验结果表明,BiLSTM-CRF取得比CRF更好的性能,其中BiLSTM-CRF的平均F1值为83.39%。
Abstract
Attribute recognition is aimed at obtaining attribute values of entities from unstructured text. In order to extract person attributes from text, a large amount of annotated data is usually needed, which is not availabel so far. To address this issue, we use Infobox of encyclopedia web pages to construct the tuples of person attributes, and then apply distant supervision method to obtain large-scale and multi-category annotated datasets for person attributes, thus avoiding the tedious process of manual annotation. Additionally, we present two kinds of models based on CRF and BiLSTM-CRF for person attribute recognition as the baseline systems. The experimental results show that BiLSTM-CRF performs better than CRF on this newly built dataset.
关键词
属性抽取 /
标注数据 /
远程监督
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Key words
attribute recognition /
annotated data /
distant supervision
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脚注
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基金
国家自然科学基金(61525205,61876115)
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