ChemSpot: a hybrid system for chemical named entity recognition
ChemSpot: a hybrid system for chemical named entity recognition
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DOI：
10.1093/bioinformatics/bts183
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发表时间：
2012-06-15
期刊：
影响因子：
5.8
通讯作者：
Leser, Ulf
中科院分区：
文献类型：
--
作者：
Rocktaschel, Tim;Weidlich, Michael;Leser, Ulf
动机：准确识别文本中的化学物质对许多应用都很重要，包括计算机辅助重建新陈代谢网络或检索药物开发中物质的信息。但由于这类分子的命名惯例和传统的多样性，这项任务非常复杂，需要计算工具的支持。结果：我们提出了一种命名实体识别(NER)工具，用于识别自然语言文本中提及的化学物质，包括琐碎的名称、药物、缩写、分子式和国际纯化学和应用化学实体联盟。由于不同类别的相关实体具有相当不同的命名特征，ChemSpot使用了一种将条件随机字段与词典相结合的混合方法。它在SCAI语料库上的F-1得分为68.1%，比其他唯一免费提供的化学NER工具OSCAR4高出10.8个百分点。
Motivation: The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be supported by computational tools.Results: We present ChemSpot, a named entity recognition (NER) tool for identifying mentions of chemicals in natural language texts, including trivial names, drugs, abbreviations, molecular formulas and International Union of Pure and Applied Chemistry entities. Since the different classes of relevant entities have rather different naming characteristics, ChemSpot uses a hybrid approach combining a Conditional Random Field with a dictionary. It achieves an F-1 measure of 68.1% on the SCAI corpus, outperforming the only other freely available chemical NER tool, OSCAR4, by 10.8 percentage points.