Anjuum Khanna – Top 3 Natural Language Process projects for Beginners
                     Anjuum Khanna – Top 3 Natural Language Process 
projects for Beginners
Anjuum Khanna – Natural Language Processing or NLP is an AI 
segment worried about the connection between human 
language and PCs. At the point when you are a novice in the 
field of programming advancement, it may very well be 
interesting to discover NLP extends that coordinate your 
adapting needs. Along these lines, we have examined a few 
guides to kick you off. Thus, on the off chance that you are a 
Machine Learning amateur, the best thing you can accomplish 
is work on some NLP ventures.
The practical approach as a theoretical approach alone won’t 
be of help in an ongoing workplace. In this article, we will 
investigate some fascinating NLP ventures which learners can 
chip away at to put their insight to test. In this article, you will 
discover top NLP venture thoughts for apprentices to get 
involved in NLP.
With regards to professions in software development, it is 
an unquestionable requirement for hopeful designers to 
chip away at their own ventures. Growing genuine tasks is 
the most ideal approach to sharpen your aptitudes and 
emerge your hypothetical information into pragmatic 
experience. 
NLP is tied in with examining and speaking to human 
language computationally. It prepares PCs to react 
utilizing setting hints much the same as a human would. 
Some regular uses of NLP around us incorporate spell 
check, auto complete, spam channels, voice text 
informing, and menial helpers like Alexa, Siri, and so forth 
As you begin dealing with NLP ventures, you won’t simply 
have the option to test your qualities and shortcomings, 
yet you will likewise pick up presentation that can be 
massively useful to help your profession. Over the most 
recent couple of years, NLP has earned extensive 
consideration across ventures. Also, the ascent of 
advancements like content and discourse acknowledgment, 
feeling investigation, and machine-to-human interchanges, 
has roused a few developments. Examination proposes that 
the worldwide NLP market will hit US$ 28.6 billion in 
market incentives in 2026.
With regards to building genuine applications, information 
on AI fundamentals is vital. In any case, it isn’t fundamental 
to have a concentrated foundation in arithmetic or 
hypothetical software engineering. 
With an undertaking based methodology, you can create 
and prepare your models even without specialized 
accreditations. Get familiar with NLP Applications. 
To help you in this excursion, we have assembled a 
rundown of NLP venture thoughts, which are propelled 
by genuine software items sold by organizations. You can 
go through these assets to brush your ML basics, 
comprehend their applications, and get new aptitudes 
during the execution stage. The more you explore 
different avenues regarding diverse NLP extends, the 
more information you pick up.
Top 3 NLP projects for Beginners  by Anjuum 
Khanna
ChatterBot
This project is available on Github. Created by Günter Cox. 
ChatterBot is a machine learning, conversational dialog engine for 
creating chat bots. ChatterBot is a machine-learning based 
conversational dialog engine build in Python which makes it possible 
to generate responses based on collections of known conversations. 
The language independent design of ChatterBot allows it to be 
trained to speak any language.
For Example, System Working –
user: Good morning! How are you doing?
bot: I am doing very well, thank you for asking.
user: You’re welcome.
bot: Do you like hats?
Gunther Cox said, “An untrained instance of ChatterBot starts off 
with no knowledge of how to communicate. Each time a user 
enters a statement, the library saves the text that they entered and 
the text that the statement was in response to. As ChatterBot 
receives more input the number of responses that it can reply and 
the accuracy of each response in relation to the input statement 
increase. The program selects the closest matching response by 
searching for the closest matching known statement that matches 
the input, it then returns the most likely response to that statement 
based on how frequently each response is issued by the people the 
bot communicates with.”
Text Generator
This project is available on Github. Created by Shiv 
Sondhi. A text generator made in python3 using Keras 
and Tensor Flow, that takes a single word/character as a 
seed and generates text using that seed. Takes an input 
word or character and generates text either character-
by-character or word-by-word. There are two different 
files for each technique (char-by-char and word-by-
word). The code is implemented using keras and tensor 
flow in python 3. The two main modes in both 
textGenerator.py files are trained and generated. The 
word-by-word file has an extra mode called retrain and 
the char-by-char file has an extra mode called exp. These 
modes are explained in the Files section.
Shiv Sondhi said, “The loss and number of epochs for 
each trial is included in the headers of both of the files. 
Both of the techniques have their advantages and 
disadvantages. The results of the char-by-char 
algorithm are slightly better than the word-by-word 
algorithm in the sense that after a point of training the 
repetition of certain characters completely disappears. 
In the word-by-word model, repetition is a problem 
even with relatively low loss. On the flip side, the 
word-by-word generator is guaranteed to make at least 
some sense, since we are dealing directly with words. 
The char-by-char model does not really make much 
sense even at its lowest loss.”
Customer Support Chatbot
This project is available on Github. Created by Momchil 
Hardalov. Momchil Hardalov said, “Recent years have seen 
growing interest in conversational agents, such as chatbots, 
which are a very good fit for automated customer support 
because the domain in which they need to operate is narrow. 
This interest was in part inspired by recent advances in 
neural machine translation, esp. the rise of sequence-to-
sequence (seq2seq) and attention-based models such as 
the Transformer, which have been applied to various other 
tasks and have opened new research directions in question 
answering, chatbots, and conversational systems. Still, in 
many cases, it might be feasible and even preferable to use 
simple information retrieval techniques. Thus, here we 
compare three different models:(i) a retrieval model, (ii) a 
sequence-to-sequence model with attention, and (iii) 
Transformer. Our experiments with the Twitter Customer 
Support Dataset, which contains over two million posts 
from customer support services of twenty major brands, 
show that the seq2seq model outperforms the other two 
in terms of semantics and word overlap.”
About Anjuum Khanna, Tech Blogger
Anjuum Khanna a strategic leader with a proven track 
record of over 19 years in spread heading profitable 
ventures within Fintech, eCom Startups, BPOs, Telecom & 
D2H, spearheaded domestic & Global Business Operations 
with large team sizes. Championed change management & 
enterprise wise automation initiatives within organizations 
in India & Middle East. Presently working as Vice President 
at Mswipe Technologies.