This work presents a framework to classify and evaluate distinct research abstract texts which are focused on the description of processes and their applications. In this context, this paper proposes natural language processing algorithms to classify, segment and evaluate the results of scientific work. Initially, the proposed framework categorize the abstract texts into according to the problems intended to be solved by employing a text classification approach. Then, the abstract text is segmented into problem description, methodology and results. Finally, the methodology of the abstract is ranked based on the sentiment analysis of its results. The proposed framework allows us to quickly rank the best methods to solve specific problems. To validate the proposed framework, oil production anomaly abstracts were experimented and achieved promising results.
翻译:这项工作为对不同的研究摘要文本进行分类和评价提供了一个框架,侧重于对过程及其应用的描述;在这方面,本文件提出了自然语言处理算法,对科学工作的结果进行分类、分类和评价;最初,拟议框架根据拟采用文本分类方法解决的问题对摘要文本进行分类;然后,将摘要文本分为问题说明、方法和结果;最后,抽象方法根据对结果的情绪分析排列;拟议框架使我们能够迅速对解决具体问题的最佳方法进行排序;为验证拟议框架,对石油生产异常摘要进行了试验,并取得了有希望的结果。