Varsha Mittal, Durgaprasad Gangodkar Bhaskar Pant, N. Chandran
{"title":"A Deep Insight in Challenges of Natural Language Processing and Usage of Deep Learning","authors":"Varsha Mittal, Durgaprasad Gangodkar Bhaskar Pant, N. Chandran","doi":"10.35940/ijitee.l1014.10812s319","DOIUrl":null,"url":null,"abstract":"Natural Language Processing (NLP) using the power\nof artificial intelligence has empowered the understanding of the\nlanguage used by human. It has also enhanced the effectiveness\nof the communication between human and computers. The\ncomplexity and diversity of the huge datasets have raised the\nrequirement for automatic analysis of the linguistic data by using\ndata-driven approaches. The performance of the data-driven\napproaches is improved after the usage of different deep learning\ntechniques in various application areas of NLP like Automatic\nSpeech Recognition, POS tagging etc. The paper addresses the\nchallenges faced in NLP and the use of deep learning techniques\nin different application areas of NLP.","PeriodicalId":83853,"journal":{"name":"Pharmeuropa. Special issue biologicals","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmeuropa. Special issue biologicals","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35940/ijitee.l1014.10812s319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Natural Language Processing (NLP) using the power
of artificial intelligence has empowered the understanding of the
language used by human. It has also enhanced the effectiveness
of the communication between human and computers. The
complexity and diversity of the huge datasets have raised the
requirement for automatic analysis of the linguistic data by using
data-driven approaches. The performance of the data-driven
approaches is improved after the usage of different deep learning
techniques in various application areas of NLP like Automatic
Speech Recognition, POS tagging etc. The paper addresses the
challenges faced in NLP and the use of deep learning techniques
in different application areas of NLP.