Exploring Deep Knowledge Resources in Biomedical Name Recognition
Exploring Deep Knowledge Resources in Biomedical Name Recognition
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DOI：
10.3115/1567594.1567616
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发表时间：
2004-08
期刊：
影响因子：
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通讯作者：
Guodong Zhou;Jian Su
中科院分区：
文献类型：
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作者：
Guodong Zhou;Jian Su
本文提出了一种面向生物医学领域的命名实体识别系统。为了处理生物医学领域中的特殊现象，提出了各种证据特征，并通过隐马尔可夫模型(HMM)进行了集成。此外，本文还提出了一种支持向量机+Sigmoid的方法来解决系统中的数据稀疏问题。除了广泛使用的构词模式、词法模式、域外词性和语义触发器等词汇级特征外，我们还研究了名称别名现象、层叠实体名称现象、使用训练语料库的封闭词典和来自数据库术语表SwissProt和别名列表LocusLink的开放词典、使用Genia语料库的缩略语解析和域内词性。
In this paper, we present a named entity recognition system in the biomedical domain. In order to deal with the special phenomena in the biomedical domain, various evidential features are proposed and integrated through a Hidden Markov Model (HMM). In addition, a Support Vector Machine (SVM) plus sigmoid is proposed to resolve the data sparseness problem in our system. Besides the widely used lexical-level features, such as word formation pattern, morphological pattern, out-domain POS and semantic trigger, we also explore the name alias phenomenon, the cascaded entity name phenomenon, the use of both a closed dictionary from the training corpus and an open dictionary from the database term list SwissProt and the alias list LocusLink, the abbreviation resolution and indomain POS using the GENIA corpus.