TrigNER: automatically optimized biomedical event trigger recognition on scientific documents.
TrigNER: automatically optimized biomedical event trigger recognition on scientific documents.
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DOI：
10.1186/1751-0473-9-1
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发表时间：
2014-01-08
影响因子：
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通讯作者：
Oliveira JL
中科院分区：
文献类型：
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作者：
Campos D;Bui QC;Matos S;Oliveira JL
细胞事件在理解生物过程和功能中发挥着核心作用，提供了对生理和发病机制的见解。从文献中自动提取此类事件的提及代表了对生物医学领域进步的重要贡献，可以更快地更新现有知识。识别指示事件的触发词是事件提取管道中非常重要的步骤，因为以下任务依赖于其输出。此步骤提出了各种复杂且未解决的挑战，即信息特征的选择、文本上下文的表示以及给定该上下文的触发词的特定事件类型的选择。我们提出了 TrigNER，一种基于机器学习的生物医学事件触发识别解决方案，它利用条件随机场 (CRF) 和高端特征集，包括基于语言的、正字法、形态学、局部上下文和依存解析特征。此外，完全可配置的算法用于自动优化每种事件类型的特征集和训练参数。因此，它会自动选择有积极贡献的特征，并自动优化 CRF 模型阶数、n-gram 大小、顶点信息和依赖解析特征的最大跳数。最终输出由各种 CRF 模型组成，每个模型都针对每种事件类型的语言特征进行了优化。 TrigNER 在 BioNLP 2009 共享任务语料库中进行了测试，总 F 测量值为 62.7，在各种事件触发类型（即基因表达、转录、蛋白质分解代谢、磷酸化和结合）上均优于现有解决方案。所提出的解决方案使研究人员能够轻松应用复杂且优化的技术来识别生物医学事件触发因素，使其应用成为一项简单的常规任务。我们相信这项工作对生物医学文本挖掘社区做出了重要贡献，有助于改进和更快地识别科学文章的事件，以及随后的假设生成和知识发现。该解决方案可作为开源免费获得：http://bioinformatics.ua.pt/trigner。
Cellular events play a central role in the understanding of biological processes and functions, providing insight on both physiological and pathogenesis mechanisms. Automatic extraction of mentions of such events from the literature represents an important contribution to the progress of the biomedical domain, allowing faster updating of existing knowledge. The identification of trigger words indicating an event is a very important step in the event extraction pipeline, since the following task(s) rely on its output. This step presents various complex and unsolved challenges, namely the selection of informative features, the representation of the textual context, and the selection of a specific event type for a trigger word given this context. We propose TrigNER, a machine learning-based solution for biomedical event trigger recognition, which takes advantage of Conditional Random Fields (CRFs) with a high-end feature set, including linguistic-based, orthographic, morphological, local context and dependency parsing features. Additionally, a completely configurable algorithm is used to automatically optimize the feature set and training parameters for each event type. Thus, it automatically selects the features that have a positive contribution and automatically optimizes the CRF model order, n-grams sizes, vertex information and maximum hops for dependency parsing features. The final output consists of various CRF models, each one optimized to the linguistic characteristics of each event type. TrigNER was tested in the BioNLP 2009 shared task corpus, achieving a total F-measure of 62.7 and outperforming existing solutions on various event trigger types, namely gene expression, transcription, protein catabolism, phosphorylation and binding. The proposed solution allows researchers to easily apply complex and optimized techniques in the recognition of biomedical event triggers, making its application a simple routine task. We believe this work is an important contribution to the biomedical text mining community, contributing to improved and faster event recognition on scientific articles, and consequent hypothesis generation and knowledge discovery. This solution is freely available as open source at http://bioinformatics.ua.pt/trigner.