Exploring Various Knowledge in Relation Extraction
Exploring Various Knowledge in Relation Extraction
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DOI：
10.3115/1219840.1219893
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发表时间：
2005-06
期刊：
影响因子：
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通讯作者：
Guodong Zhou;Jian Su;Jie Zhang;Min Zhang
中科院分区：
文献类型：
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作者：
Guodong Zhou;Jian Su;Jie Zhang;Min Zhang
提取实体之间的语义关系具有挑战性。本文研究了支持向量机在基于特征的关系提取中结合多种词汇、句法和语义知识。我们的研究表明，基本短语分块信息对关系提取非常有效，并且对句法方面的性能提升贡献很大，而来自全解析的附加信息对关系提取的进一步提升作用有限。这表明，用于关系提取的完整解析树中的大多数有用信息都是浅层的，可以通过分块捕获。我们还演示了如何将语义信息（如WordNet和Name List）用于基于特征的关系提取，以进一步提高性能。对ACE语料库的评估表明，有效地结合各种特征使我们的系统在24种ACE关系亚型上优于以前报道的最好的系统，并且在5种ACE关系类型的F-measure中显著优于基于树核的系统。
Extracting semantic relationships between entities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.