文章基本信息

标题：Natural Language Processing Applications: A New Taxonomy using Textual Entailment
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作者：Manar Elshazly ; Mohammed Haggag ; Soha Ahmed Ehssan 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：5
页码：676
DOI：10.14569/IJACSA.2021.0120580
出版社：Science and Information Society (SAI)
摘要：Textual entailment recognition is one of the recent challenges of the Natural Language Processing (NLP) domain. Deep learning strategies are used in the work of text entailment instead of traditional Machine learning or raw coding to achieve new enhanced results. Textual entailment is also used in the substantial applications of NLP such as summarization, machine translation, sentiment analysis, and information verification. Text entailment is more precise than traditional Natural Language Processing techniques in extracting emotions from text because the sentiment of any text can be clarified by textual entailment. For this purpose, when combining a textual entailment with deep learning, they can hugely showed an improvement in performance accuracy and aid in new applications such as depression detection. This paper lists and describes applications of natural language processing regarding textual entailment. Various applications and approaches are discussed. Moreover, datasets, algorithms, resources, and performance evaluation for each model is included. Also, it compares textual entailment application models according to the method used, the result for each model, and the pros and cons of each model.
关键词：Textual entailment; deep learning; summarization; sentiment analysis; information verification; machine learning; depression detection