Open Information Extraction with Meta-pattern Discovery in Biomedical Literature
Open Information Extraction with Meta-pattern Discovery in Biomedical Literature
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DOI：
10.1145/3233547.3233594
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Xuan Wang;Yu Zhang;Qi Li;Yinyin Chen;Jiawei Han
中科院分区：
文献类型：
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作者：
Xuan Wang;Yu Zhang;Qi Li;Yinyin Chen;Jiawei Han
生物医学开放信息抽取（BioOpenIE）是一种在没有或很少有人监督的情况下从非结构化文本中自动抽取结构化信息的新范式。它不需要任何预先指定的关系类型，但旨在从语料库中提取所有的关系元组。开放信息提取（OpenIE）的一个主要挑战是，它产生大量的表面名称形成的关系元组，不能直接用于下游应用程序。我们提出了一个新的框架CPIE（子句+模式引导的信息提取），结合子句提取和元模式发现提取结构化的关系元组很少监督。与以前的OpenIE方法相比，CPIE产生大量但更结构化的输出，可以直接用于下游应用程序。我们首先从输入的句子中检测出短句。然后，我们提取质量文本模式和执行同义模式分组，以确定关系类型。最后，通过匹配文本中的每个质量模式，得到相应的关系元组。实验结果表明，CPIE在保持关系元组的独特性和简单性的同时，获得了最高的精度。CPIE在处理句子结构复杂、信息量大的生物医学文献方面显示出巨大的潜力。
Biomedical open information extraction (BioOpenIE) is a novel paradigm to automatically extract structured information from unstructured text with no or little supervision. It does not require any pre-specified relation types but aims to extract all the relation tuples from the corpus. A major challenge for open information extraction (OpenIE) is that it produces massive surface-name formed relation tuples that cannot be directly used for downstream applications. We propose a novel framework CPIE (Clause+Pattern-guided Information Extraction) that incorporates clause extraction and meta-pattern discovery to extract structured relation tuples with little supervision. Compared with previous OpenIE methods, CPIE produces massive but more structured output that can be directly used for downstream applications. We first detect short clauses from input sentences. Then we extract quality textual patterns and perform synonymous pattern grouping to identify relation types. Last, we obtain the corresponding relation tuples by matching each quality pattern in the text. Experiments show that CPIE achieves the highest precision in comparison with state-of-the-art OpenIE baselines, and also keeps the distinctiveness and simplicity of the extracted relation tuples. CPIE shows great potential in effectively dealing with real-world biomedical literature with complicated sentence structures and rich information.