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NERChem：通过全词元特征和具有化学子类组成的命名实体特征，使NERBio适用于化学专利。

NERChem: adapting NERBio to chemical patents via full-token features and named entity feature with chemical sub-class composition.

作者信息

Tsai Richard Tzong-Han, Hsiao Yu-Cheng, Lai Po-Ting

出版信息

Database (Oxford). 2016 Oct 25;2016:baw135. doi: 10.1093/database/baw135.

DOI:10.1093/database/baw135
PMID:31414701
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5091336/
Abstract

Chemical patents contain detailed information on novel chemical compounds that is valuable to the chemical and pharmaceutical industries. In this paper, we introduce a system, NERChem that can recognize chemical named entity mentions in chemical patents. NERChem is based on the conditional random fields model (CRF). Our approach incorporates ( 1 ) class composition, which is used for combining chemical classes whose naming conventions are similar; ( 2 ) BioNE features, which are used for distinguishing chemical mentions from other biomedical NE mentions in the patents; and ( 3 ) full-token word features, which are used to resolve the tokenization granularity problem. We evaluated our approach on the BioCreative V CHEMDNER-patent corpus, and achieved an F-score of 87.17% in the Chemical Entity Mention in Patents (CEMP) task and a sensitivity of 98.58% in the Chemical Passage Detection (CPD) task, ranking alongside the top systems. Database URL: Our NERChem web-based system is publicly available at iisrserv.csie.n cu.edu.tw/nerchem.

摘要

化学专利包含有关新型化合物的详细信息，这些信息对化学和制药行业具有重要价值。在本文中，我们介绍了一种名为NERChem的系统，它能够识别化学专利中提及的化学命名实体。NERChem基于条件随机场模型（CRF）。我们的方法包括：（1）类组合，用于组合命名惯例相似的化学类别；（2）BioNE特征，用于在专利中区分化学提及与其他生物医学命名实体提及；（3）全词元词特征，用于解决词元化粒度问题。我们在BioCreative V CHEMDNER-专利语料库上评估了我们的方法，在专利中的化学实体提及（CEMP）任务中获得了87.17%的F值，在化学段落检测（CPD）任务中获得了98.58%的灵敏度，与顶级系统并列。数据库网址：我们基于网络的NERChem系统可在iisrserv.csie.n cu.edu.tw/nerchem上公开获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e7/5091336/d20558d14a0c/baw135f6p.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e7/5091336/a7e94237669d/baw135f1p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e7/5091336/2edf22e7e815/baw135f2p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e7/5091336/d11c773b044b/baw135f3p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e7/5091336/a2607b78f622/baw135f4p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e7/5091336/e6a52c6389c6/baw135f5p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95e7/5091336/d20558d14a0c/baw135f6p.jpg

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本文引用的文献

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Optimising chemical named entity recognition with pre-processing analytics, knowledge-rich features and heuristics.通过预处理分析、知识丰富的特征和启发式方法优化化学命名实体识别。
J Cheminform. 2015 Jan 19;7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S6. doi: 10.1186/1758-2946-7-S1-S6. eCollection 2015.
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tmChem: a high performance approach for chemical named entity recognition and normalization.
复杂关系的提取及其转化为生物表达语言（BEL）——BioCreative VI（2017） BEL 赛道概述。
Database (Oxford). 2019 Jan 1;2019. doi: 10.1093/database/baz084.
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Statistical principle-based approach for gene and protein related object recognition.基于统计原理的基因和蛋白质相关对象识别方法。
J Cheminform. 2018 Dec 17;10(1):64. doi: 10.1186/s13321-018-0314-7.
tmChem：一种用于化学命名实体识别和标准化的高性能方法。
J Cheminform. 2015 Jan 19;7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S3. doi: 10.1186/1758-2946-7-S1-S3. eCollection 2015.
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CheNER: a tool for the identification of chemical entities and their classes in biomedical literature.CheNER：一个用于在生物医学文献中识别化学实体及其类别的工具。
J Cheminform. 2015 Jan 19;7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S15. doi: 10.1186/1758-2946-7-S1-S15. eCollection 2015.
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J Cheminform. 2015 Jan 19;7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S14. doi: 10.1186/1758-2946-7-S1-S14. eCollection 2015.
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