Natural language processing is an important area for future PhD research. Recent trends in NLP research include using techniques like opinion mining and content analysis to analyze user reviews and news articles. The document outlines many potential topics in NLP that PhD students could explore like developing personalized educational materials using medical records or predicting clinical outcomes with deep learning models.

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. “Natural Language Processing is a theoretically
motivated 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.
What is Natural
Language
Processing
Contd...

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

8. Smart officialdoms now make decisions based not on data
only but on the intelligence derived from that data by NLP-
powered machines.
As AI tries to take advantage of the technology’s prospects,
NLP would get even more advanced.
MASSIVE SHIFT FROM DATA-DRIVEN TO INTELLIGENCE-
DRIVEN DECISION MAKING
Future
of NLP
Contd...

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.
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.
CREATION OF MORE EXTENSIVE, BETTER NLP PLATFORMS LIKE SPARK NLP
ERADICATION OF HUMAN DATA SCIENTISTS
Contd...

11. 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.
A SWAP TO NATURAL LANGUAGE

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

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

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