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Review on natural language processing tasks for text documents

机译:审查文本文档的自然语言处理任务

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This paper mainly focused on Natural Language Processing (NLP) tasks such as Coreference resolution, Discourse Analysis, Named Entity Recognition (NER), Sentiment Analysis, Word sense disambiguation (WSD), Part of Speech (POS), etc. It also reviewed each NLP task with various application areas, with their different approaches and their corresponding methods. This survey is done to decide which NLP task will be better for preprocessing of search keyword, which in turn uses for appropriate matching to desired text documents. Finally it comes to a conclusion that POS tagging and chunking, both will be a better option for preprocessing of keyword, so that its resultant keyword will give desired and important text document.
机译:本文主要关注自然语言处理(NLP)任务,例如共指解析,语篇分析,命名实体识别(NER),情感分析,词义消歧(WSD),词性(POS)等。 NLP任务涉及各种应用领域,具有不同的方法和相应的方法。进行此调查是为了确定哪个NLP任务更适合于预处理搜索关键字,而后者又用于与所需文本文档进行适当匹配。最终得出结论,POS标记和分块将是关键字预处理的更好选择,因此其结果关键字将提供所需且重要的文本文档。

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