论文标题
通过使用基于自然语言加工的算法的摩擦搅拌搅拌焊接的信息检索
Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms
论文作者
论文摘要
文本摘要是一种将大量文本凝结成一些关键要素的技术，这些要素给出了内容的一般印象。当某人需要快速，精确的大量信息摘要时，这将变得至关重要。如果手动完成，总结文本可能会付出昂贵且耗时。自然语言处理（NLP）是人工智能的细分，它通过从数据堆中提取相关信息来缩小技术和人类认知之间的差距。在目前的工作中，从学术研究论文的摘要中收集了有关铝合金摩擦搅拌焊接的科学信息。为了从这些研究中提取相关信息摘要，使用了四种基于自然语言处理的算法，即潜在的语义分析（LSA），Luhn算法，LEX Rank Rank算法和KL-Algorithm。为了评估这些算法的准确性评分，使用了以召回为导向的研究，用于观察评估（Rouge）。结果表明，与其他算法相比，Luhn算法导致最高的F1得分为0.413。
Text summarization is a technique for condensing a big piece of text into a few key elements that give a general impression of the content. When someone requires a quick and precise summary of a large amount of information, it becomes vital. If done manually, summarizing text can be costly and time-consuming. Natural Language Processing (NLP) is the sub-division of Artificial Intelligence that narrows down the gap between technology and human cognition by extracting the relevant information from the pile of data. In the present work, scientific information regarding the Friction Stir Welding of Aluminum alloys was collected from the abstract of scholarly research papers. For extracting the relevant information from these research abstracts four Natural Language Processing based algorithms i.e. Latent Semantic Analysis (LSA), Luhn Algorithm, Lex Rank Algorithm, and KL-Algorithm were used. In order to evaluate the accuracy score of these algorithms, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) was used. The results showed that the Luhn Algorithm resulted in the highest f1-Score of 0.413 in comparison to other algorithms.
