Kabir M Ahsanul, Almulhim AlJohara, Luo Xiao, Al Hasan Mohammad
Department of Computer Science, Indiana University Purdue University Indianapolis, Indianapolis, IN USA.
Department of Computer Information and Graphics Technology, Indiana University Purdue University, Indianapolis, IN USA.
J Healthc Inform Res. 2022 May 25;6(3):295-316. doi: 10.1007/s41666-022-00116-z. eCollection 2022 Sep.
Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as causality between disease and symptoms, between medications and side effects, and between genes and diseases. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non-informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by designing an unsupervised method for cause and effect phrase extraction, PatternCausality, which is specifically suitable for the medical literature. Our proposed approach first uses a collection of cause-effect dependency patterns as template to extract head words of cause and effect phrases and then it uses a novel phrase extraction method to obtain complete and meaningful cause and effect phrases from a sentence. Experiments on a cause-effect dataset built from sentences from PubMed articles show that for extracting cause and effect entities, PatternCausality is substantially better than the existing methods-with an order of magnitude improvement in the -score metric over the best of the existing methods. We also build different variants of PatternCausality, which use different phrase extraction methods; all variants are better than the existing methods. PatternCausality and its variants also show modest performance improvement over the existing methods for extracting cause and effect entities in a domain-neutral benchmark dataset, in which cause and effect entities are nouns or noun phrases consisting of one to two words.
从医学文献中提取因果实体是医学信息检索中的一项重要任务。解决此任务的一种方法可用于编译各种因果关系，例如疾病与症状之间、药物与副作用之间以及基因与疾病之间的因果关系。现有的提取因果实体的方法对于因果短语为命名实体、单字名词或由两到三个单词组成的名词短语的句子效果良好。不幸的是，在医学文献中，句子中的因果短语并非简单的名词或名词短语，而是由几个单词组成的复杂短语，现有方法无法正确提取此类句子中的因果实体。与给定句子中预期的完整提取相比，因果实体的部分提取传达的质量较差、信息不足且往往相互矛盾。在这项工作中，我们通过设计一种无监督的因果短语提取方法PatternCausality来解决此问题，该方法特别适用于医学文献。我们提出的方法首先使用一组因果依赖模式作为模板来提取因果短语的中心词，然后使用一种新颖的短语提取方法从句子中获取完整且有意义的因果短语。对从PubMed文章句子构建的因果数据集进行的实验表明，对于提取因果实体，PatternCausality比现有方法要好得多——在F1分数指标上比现有最佳方法有一个数量级的提升。我们还构建了PatternCausality的不同变体，它们使用不同的短语提取方法；所有变体都比现有方法更好。在一个领域中立的基准数据集中，因果实体是由一到两个单词组成的名词或名词短语，PatternCausality及其变体在提取因果实体方面也比现有方法有适度的性能提升。
