Natural Language Processing is one of the branches of data science that systematically deals with analyzing, understanding, and extracting information from the text data. By using the techniques of Natural Language Processing, one can organize and analyze massive chunks of text data and perform numerous automated tasks to solve a wide range of problems such as automatic summarization, machine translation, and many more.
Let’s have a quick look at the application of Natural Language Processing.
Finally, we come to the topic of what tools and libraries are mostly used in Natural Language Processing.
Here,we will discuss the most-used tools and libraries. The list is not limited to the things we discuss below, there are a plenty of other tools for dealing with NLP tasks.
A regular expression or regex is a sequence of characters that define a search pattern. Regular Expressions use patterns to extract information from a given piece of text. At the same time, they are used for other useful NLP tasks like cleaning/filtering unnecessary symbols and searching for a given pattern in the text.
Natural Language Tool Kit or NLTK is one of the most popular NLP libraries in
Python. It supports a plethora of tasks and can be used to do anything from text pre-processing techniques like stopping word removal, tokenization, stemming, and lemmatization to building
spaCy is considered to be a successor of NLTK and is known as an industrial grade natural language processing library. It is scalable and uses the latest neural network based models to perform tasks like named entity recognition, parts of speech tagging,sentence dependency mapping, etc.
Gensim is an open-source library for unsupervised topic modeling and natural language processing that uses modern statistical machine learning. It is extensively used when working with word embeddings like
Doc2Vec, and also when one has to perform topic modeling related tasks.
FastText is a library for efficient learning of word representations and sentence classification. This library is the center of attraction for the NLP community and a perfect substitution to the
gensim package, which provides the functionality of Word Vectors, etc.
TextBlobs is a beginner-friendly NLP library that is built on the basis of the NLTK and Pattern. A few key advantages are: it is easy to learn and has a lot of features like sentiment analysis, POS-tagging, noun phrase extraction,etc. TextBlobs is the perfect library for the NLP beginners.
Stanford NLP is a library that is straight out of Stanford’s NLP Research Group and lets you perform text pre-processing on more than 53 human languages! Adding to that, it is incredibly fast and serves as an interface for the legendary NLP toolkit from Stanford that is Core NLP tools.
Flair is a plain and simple natural language processing (NLP) library developed and open-sourced by Zalando Research. Flair’s framework is created using PyTorch. The Zalando Research team has also released several pre-trained models for the following NLP tasks:
Regex can sometimes be really slow when working on large documents – FlashText is a new library that is faster than regular expressions for NLP pre-processing tasks. FlashText is a Python library created specifically for the purpose of searching and replacing words in a document. The way FlashText works is it requires a word or a list of words and a string. The words that FlashText calls keywords are then searched or replaced in the string.
This library is good for people who want to try the latest groundbreaking models in NLP without waiting for it. The recently released
Pytorch-Transformers brings state-of-the-art NLP models like
XLNet, and Transformers-XL to Python.
We have discussed 10 tools and libraries, but ,as I already said, this is not it. There are still many other tools and libraries, which I have named below:
I hope you got to learn something new and will try out all these tools and libraries to build something cool!!
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