Future of Natural Language Processing - Potential Lists of Topics for PhD students - Phdassistance
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The document discusses the future of Natural Language Processing (NLP) and its significance in research, emphasizing its evolution from basic tasks to more advanced applications powered by machine learning and artificial intelligence. It highlights recent trends in NLP, including the development of personalized educational materials and clinical decision-making techniques, as well as the growing importance of NLP in various fields. The document also points out the potential research gaps in NLP, making it an attractive area for PhD students.
![Analysing news articles using NLP by Titiliuc, Ruseti, & Dascalu (2020[1])–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.](https://image.slidesharecdn.com/futureofnaturallanguageprocessing-potentiallistsoftopicsforphdstudents-phdassistance-210420111334/85/Future-of-Natural-Language-Processing-Potential-Lists-of-Topics-for-PhD-students-Phdassistance-14-320.jpg)
Future of Natural Language Processing - Potential Lists of Topics for PhD students - Phdassistance
- 1. FUTURE OF NATURAL LANGUAGE PROCESSING- POTENTIAL LISTS OF TOPICS FOR PHD STUDENTS An Academic presentation by Dr . Nancy Agnes, Head, Technical Operations, Phdassistance Group www.phdassistance.com Email: info@phdassistance.com
- 2. Introduction What is Natural Language Processing Future of NLP Recent trends in the NLP from Scholarly Papers published in Scopus Indexed Journals Data Sets for NLP Conclusion Outline TODAY'S DISCUSSION
- 3. 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. Introduction
- 4. motivated “Natural Language Processing is a theoretically range of computational techniques for analyzing 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. Contd... What is Natural Language Processing
- 5. 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. Contd...
- 6. 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.
- 8. 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. Contd... As AI tries to take advantage of the technology’s prospects, NLP would get even more advanced. Future of NLP
- 10. 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. ERADICATION OF HUMAN DATA SCIENTISTS Data scientists would be wiped out in the future as NLP advances along with machine l earning, and its features such as pattern recognition, advanced analysis, and interpretation improve beyond today’s level. Contd... CREATION OF MORE EXTENSIVE, BETTER NLP PLATFORMS LIKE SPARK NLP
- 11. 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.
- 12. Recently, analysing the user reviews, Aldabbas et al., (2021) scrapped google play content and knowledge engineering. 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). Multi-class Categorization of design build contract technique requirement were built using text mining and NLP by Hassan et al., (2020) Contd... Recent trends in the NLP from Scholarly Papers published in Scopus Indexed Journals
- 13. 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.
- 14. Analysing news articles using NLP by Titiliuc, Ruseti, & Dascalu (2020[1])–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.
- 15. Feedback from users in app stores Social media and developer comments in discussion forums Patient story – qualitative interview, voice recording Newspaper articles Customer review Social media comments Stories Content from the Manuscripts / Journal articles and many more Data Sets for NLP
- 16. NLP can analyze 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 support 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. Conclusion