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Future of Natural Language Processing – Potential Lists of Topics for PhD students

April 19, 2021 PhD Assistance
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Categories of PhdAssistance Dissertation

The talent to develop a Good Research Topic is a skill. An instructor may allocate you a specific topic, but instructors often require you to select your topic of interest. If you have chosen Natural Language Processing (NLP) as your research topic, your research work would be incredible. We discover the opportunities (2021) and upcoming trends below.

What is Natural Language Processing : Definition: “Natural Language Processing is a theoretically motivated range of computational techniques for analysing and representing naturally occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications”.

NLP technology facilitates the machines to read, understand, analyze, and gather appropriate sense from human languages. NLP is also recognized as Computational Linguistics, a blend of two technologies, including Machine Learning (ML) and Artificial Intelligence (AI). While human communicates with machines, everything would work faster and better because of NLP technology. 20 years ago, NLP technology was under development; hence it was only in limited use. In the past decade, NLP holds a fantastic addition to daily life but still it has only reached the lexical and syntactic processing levels for full-fledge English, with limited semantic capabilities.

NLP is the driving force behind several applications, which we are using in our daily life.

  • Microsoft Word that employs NLP to identify and correct for errors in spelling and sentence organization
  • Google Translate, which is a Language translation application
  • Siri, OK Google, Alexa, and Cortana
  • Interactive Voice Response (IVR) apps which act as personal assistant applications

 

Most used NLP Tools.

 

 Most used NLP resource Long tail distributed NLP technologies Long Tail NLP technique
  • Stanford Core NLP
  • GATE
  • NLTK
  • Apache OpenNLP
  • WEKA
  • QTag POS Tagger
  •  Morphy
  •  Mate Tools
  •  MALLET Toolkit
  •  Link Grammar Parser
  • LOLITA NLP system
  • Google Speech API
  • Flex/Lex
  • Chamiak Parser
  • POS Tagger
  • Gensim
  • Google data API
  • IBM Watson System
 WordNetVerbNet

British

National Corpus

GermanNet

FrameNET

CM-1

DBPedia

Homby’s Verb Patterns

Google News Corpus

MUC Shared Tasks Datasets

Open American National Corpus

Brown Corpus

 POS tagging

Stanford CoreNLP

WordNet

 Word EmbeddingDoc2Vec

LSTM

CNN

RNN

 

Future of NLP

As AI tries to take advantage of the technology’s prospects, NLP would get even more advanced.

  1. The massive shift from data-driven to intelligence-driven decision making

Smart officialdoms now make decisions based not on data only but on the intelligence derived from that data by NLP-powered machines.

  1. Creation of more extensive, better NLP platforms like Spark NLP

Data scientists dealing with NLP and other AI aspects rely on NLP library platforms to construct and trial their applications. The platform pool such as OpenNMT, Stanford’s CoreNLP, SpaCy, and Tensor Flow has been widely used.

  1. Eradication of human data scientists

Data scientists would be wiped out in the future as NLP advances along with Machine Learning, and its features such as pattern recognition, advanced analysis, and interpretation improve beyond today’s level.

  1. A swap to natural language

The future test in NPL would be able to understand the human language. In the future, natural language processing would have to evolve in its function to become natural language understanding.

Recent trends in the NLP from Scholarly Papers published in Scopus Indexed Journals

  • Recently, analysing the user reviews, Aldabbas et al., (2021) scrapped google play content and knowledge engineering1.
  • Food recipes were altered and generated with NLP techniques by Pan et al., (2020)
  • Construct identity problem were tackled using NLP techniques by Ludwig et al., (2020)2.
  • Multi-class Categorization of design build contract technique requirement were built using text mining and NLP by Hassan et al., (2020)3
  • Building personalized educational material for chronic disease patients by Wang et al., (2020).
  • Medical based NLP techniques: Clinical decision making with EHRs and intelligent patient summaries using ML and NLP techniques by Trappey et al., (2020). In medical, the current deep learning-based NLP techniques focus into three major purposes: representation learning, information extraction and clinical prediction.
  • Analysing news articles using NLP by Titiliuc, Ruseti, & Dascalu (2020) 4– Semantic similarities between articles and rank various publications based on their influences – Visualization to ease understanding. Techniques such as opinion mining, geographical name extraction nd content quality assessment are the future scope.

Data Sets for NLP

  1. Feedback from users in app stores
  2. Social media and developer comments in discussion forums
  3. Patient story – qualitative interview, voice recording
  4. Newspaper articles
  5. Customer review
  6. Social media comments
  7. Stories
  8. Content from the Manuscripts / Journal articles and many more

Conclusion

NLP can analyse and bond with language-based information by making machines equipped to understand the content and substitute human tasks like abstracting, translation, classification, and mining. Moreover, NLP giving organizations a way to analyze shapeless information, customer supports communications, product analyses, and social media messages. So there are many Research Gap is yet to be determined in this field. Hence it would be a good opening for the researchers to start research in this area.

References

  1. Aldabbas, H., Bajahzar, A., Alruily, M., Qureshi, A. A., Latif, R. M. A., & Farhan, M. (2021). Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews. J Intell Syst, 30(1), 192–208.
  2. Y. Pan, Q. Xu and Y. Li, “Food Recipe Alternation and Generation with Natural Language Processing Techniques,” 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW), Dallas, TX, USA, 2020, pp. 94-97, doi: 10.1109/ICDEW49219.2020.000-1.
  3.  Fahad ul Hassan; Tuyen Le; and Duc-Hoc Tran, Multi-Class Categorization of Design-Build Contract Requirements Using Text Mining and Natural Language Processing Techniques, Construction Research Congress 2020,
  4. C. Titiliuc, S. Ruseti, and M. Dascalu, “What’s Been Happening in the Romanian News Landscape? A Detailed Analysis Grounded in Natural Language Processing Techniques,” 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, 2020, pp. 195-201, doi: 10.1109/SYNASC51798.2020.00040.

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