Top 5 Python NLP Libraries to Build a Human like Applications

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Are you looking for  Python NLP Libraries? I know it really confusing to find the best one . Usually when we search it on internet , we find a big list of framework . Do not worry , This article will not overload you with tons of information . Here I will list only which are the most useful and easy to learn and implement .All you need to read this article till end for understanding  Pros and Cons for each NLP frameworks .

Top 5 Python NLP Libraries –

In Python you can try these 5 NLP framework –

  1. NLTK
  2. TEXTBLOB
  3. Stanford CoreNLP
  4. spaCy
  5. Gensim

1. NLTK

This one of the widely used NLP library in Python .Most of the researcher prefer it . It is basically recommended in academics . The drawback of NLTK is , “NLTK  is heavy ” . Using NLTK may slowdown your application  performance . That is why , In industry people avoid using NLTK but in academy and research center it is king of all other Python NLP Libraries .

It has big range of corpora and lexicons which make it more flexible .

2.Stanford CoreNLP

A great contribution by Stanford . Its fast and complete NLP  suits . As per best of my knowledge , Stanford CoreNLP covers almost all NLP component like NER parser, POS tagger etc . Most of the Industry products are built on the top of this API . Best part of  Stanford CoreNLP is its community support . You will  get almost all error trace in opensource community . Stanford CoreNLP is java based but community has provided the Python wrapper for this .

3. TextBlob

It is built on the top of NLTK .Its quite fast and production ready Python NLP Library. You can learn it very easily . here is the complete tutorial on TextBlob .   Here is the The official link for Textblob. It is as simple as you can retrieve the sentiment score in one line of code .

4.spaCy

Its newly release API in this field . The philosophy behind spaCy is to provide the specific methodology for specific purpose  . Its very different from NLTK .  NLTK give you the flexibility to choose the best algorithm and methodology but its opposite . Its automatically inbuilt most of the model and algorithms for you . This is the main reason it is production friendly and immnesly liked by  Industry .

5.Gensim

Its very specific API . Gensim is not general purpose API . I mean you should not expect the same functionality as you get in NLTK etc. So you  must be thinking  why is it so popular ? Right !

Actually It performs what ever it does in very specific and efficient than others Python NLP Libraries .

Learning Resources –

Learning from a book , video or any Blog ( Tutorial Website ) like Data Science Learner is completely you choice . I prefer blogs just for overview . Video for walk through and hands on basic knowledge . Once you have strong fundamental you should prefer some good books .

1. Data Science: Natural Language Processing (NLP) in Python (Video Course) –

High rated course on udemy for NLP . You will get hands on information for developing your own content spinner , own spam detector from scratch . It also covers all NLP basics in NLTK framework .

2. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit

Awesome book for beginner to advance level NLP learner . Most of beginner start with NLTK framework and it covers all NLP concept in NLTK . From processing and cleaning of Raw text to advance level NLP algorithms it covers every thing .

Is Python best for NLP stuffs ?

Yes . Python is not only Best for NLP stuffs but It is best for all Data Science stuffs . Specially If I talk about NLP , Python is too rich in the term of libraries and API .Python is already a mature programming language . You must get all error trace which occurs in day to day programming in  coding communities . It really reduce the development time . Python learning curve is also very smooth .

Python is extremely dynamic language . Specially when you are targeting unstructured data . This dynamic nature programming language is most prefer .

Conclusion –

NLP seems to be difficult . Its application seems like a magic . I agree NLP is difficult but there is a hidden fact that Implementing any existing NLP API is too easy .I mean If you are playing to integrate NLP into your project . Its really easy .  Obviously If you are doing any research or improving any existing model it may difficult . Because In Research scenario you are dealing with model improvement  . I hope this article worths you in finding the Right NLP framework for you .

Thanks

Data Science Learner Team.