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首页> 外文期刊>BMC Bioinformatics >Resolving anaphoras for the extraction of drug-drug interactions in pharmacological documents
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Resolving anaphoras for the extraction of drug-drug interactions in pharmacological documents

机译：解析照应用于提取药理文献中的药物相互作用

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Background Drug-drug interactions are frequently reported in the increasing amount of biomedical literature. Information Extraction (IE) techniques have been devised as a useful instrument to manage this knowledge. Nevertheless, IE at the sentence level has a limited effect because of the frequent references to previous entities in the discourse, a phenomenon known as 'anaphora'. DrugNerAR, a drug anaphora resolution system is presented to address the problem of co-referring expressions in pharmacological literature. This development is part of a larger and innovative study about automatic drug-drug interaction extraction. Methods The system uses a set of linguistic rules drawn by Centering Theory over the analysis provided by a biomedical syntactic parser. Semantic information provided by the Unified Medical Language System (UMLS) is also integrated in order to improve the recognition and the resolution of nominal drug anaphors. Besides, a corpus has been developed in order to analyze the phenomena and evaluate the current approach. Each possible case of anaphoric expression was looked into to determine the most effective way of resolution. Results An F-score of 0.76 in anaphora resolution was achieved, outperforming significantly the baseline by almost 73%. This ad-hoc reference line was developed to check the results as there is no previous work on anaphora resolution in pharmalogical documents. The obtained results resemble those found in related-semantic domains. Conclusions The present approach shows very promising results in the challenge of accounting for anaphoric expressions in pharmacological texts. DrugNerAr obtains similar results to other approaches dealing with anaphora resolution in the biomedical domain, but, unlike these approaches, it focuses on documents reflecting drug interactions. The Centering Theory has proved being effective at the selection of antecedents in anaphora resolution. A key component in the success of this framework is the analysis provided by the MMTx program and the DrugNer system that allows to deal with the complexity of the pharmacological language. It is expected that the positive results of the resolver increases performance of our future drug-drug interaction extraction system.
机译：背景技术在越来越多的生物医学文献中经常报道药物相互作用。信息提取（IE）技术已被设计为管理此知识的有用工具。但是，由于在语篇中频繁引用以前的实体，因此在句子级别的IE效果有限，这种现象称为“回指”。介绍了DrugNerAR，一种药物回指解析系统，以解决药理文献中共同引用表达的问题。这一发展是有关药物-药物相互作用自动提取的一项较大的创新研究的一部分。方法该系统在生物医学句法分析器提供的分析上使用由居中理论绘制的一套语言规则。还集成了统一医学语言系统（UMLS）提供的语义信息，以改善对正常药物照应的识别和解析。此外，已经开发了语料库以分析现象并评估当前方法。调查了每种可能的回指表达方式，以确定最有效的解决方法。结果回指解析度的F分数达到0.76，明显优于基线，提高了近73％。该临时参考线是为了检查结果而开发的，因为以前在药理学文献中没有涉及回指解析的工作。获得的结果类似于在相关语义域中发现的结果。结论本方法在药理学文献中对隐喻表达进行解释的挑战中显示出非常有希望的结果。 DrugNerAr与其他在生物医学领域解决回指解析的方法获得相似的结果，但是与这些方法不同，它专注于反映药物相互作用的文献。对中理论已被证明在回指解析的先行词选择中是有效的。该框架成功的关键因素是MMTx程序和DrugNer系统提供的分析，该分析可以处理药理语言的复杂性。可以预期，解析器的积极成果将提高我们未来的药物-药物相互作用提取系统的性能。

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