Gimli: open source and high-performance biomedical name recognition.
Gimli: open source and high-performance biomedical name recognition.
复制标题
DOI：
10.1186/1471-2105-14-54
复制
发表时间：
2013-02-15
影响因子：
3
通讯作者：
Oliveira JL
中科院分区：
文献类型：
--
作者：
Campos D;Matos S;Oliveira JL
生物医学名称的自动识别是生物医学信息提取中的一项重要任务，目前存在着一些复杂且尚未解决的挑战。近年来，已经实施了各种解决方案来解决这个问题。然而，在系统特性、可定制性和可用性方面的局限性仍然阻碍了它们在文本挖掘研究之外的更广泛应用。我们介绍了Gimli，一个开源的，最先进的工具，用于自动识别生物医学名称。Gimli包括一组扩展的已实现和用户可选择的功能，例如正字法，形态学，基于语言学，连接词和基于词典。还提供了一种简单而快速的方法来联合收割机不同的训练模型。Gimli在GENETAG和JNLPBA语料库上的F值分别为87.17%和72.23%，明显优于现有的开源解决方案。Gimli是一个现成的，即用型的命名实体识别工具，为从科学文本中识别生物医学实体提供经过训练和优化的模型。它可以用作命令行工具，提供完整的功能，包括训练新模型以及通过配置文件自定义特征集和模型参数。高级用户可以通过提供的库将Gimli集成到他们的文本挖掘工作流中，并扩展或调整其功能。基于最终用户和开发人员的底层系统特性和功能，以及报告的性能结果，我们认为Gimli是生物医学NER的最先进解决方案，有助于该领域更快，更好的研究。Gimli可以在http://bioinformatics.ua.pt/gimli上免费获得。
Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli.
登录
查看更多内容
影响因子：
5.8
作者：
Settles, B
通讯作者：
Settles, B
Genetag：一种名为实体识别的基因/蛋白质的标记语料库。
影响因子：
3
作者：
Tanabe, L;Xie, N;Thom, LH;Matten, W;Wilbur, WJ
通讯作者：
Wilbur, WJ
影响因子：
4.5
作者：
Schuemie, Martijn J.;Mons, Barend;Kors, Jan A.
通讯作者：
Kors, Jan A.
影响因子：
12.3
作者：
Smith, Larry;Tanabe, Lorraine K.;Johnson Nee Ando, Rie;Kuo, Cheng-Ju;Chung, I-Fang;Hsu, Chun-Nan;Lin, Yu-Shi;Klinger, Roman;Friedrich, Christoph M.;Ganchev, Kuzman;Torii, Manabu;Liu, Hongfang;Haddow, Barry;Struble, Craig A.;Povinelli, Richard J.;Vlachos, Andreas;Baumgartner, William A., Jr.;Hunter, Lawrence;Carpenter, Bob;Tsai, Richard Tzong-Han;Dai, Hong-Jie;Liu, Feng;Chen, Yifei;Sun, Chengjie;Katrenko, Sophia;Adriaans, Pieter;Blaschke, Christian;Torres, Rafael;Neves, Mariana;Nakov, Preslav;Divoli, Anna;Mana-Lopez, Manuel;Mata, Jacinto;Wilbur, W. John
通讯作者：
Wilbur, W. John
影响因子：
5.8
作者：
Campos, David;Matos, Sergio;Rebholz-Schuhmann, Dietrich
通讯作者：
Rebholz-Schuhmann, Dietrich