NLP or Natural Language Processing is becoming exceptionally popular these days. This is a field of artificial intelligence that helps with extracting and understanding information from texts and allows the developers to do more in less time. The main tasks of NPL are to recognize speech, generate sentiment analysis, machine translation and text analysis.
There was a time when a Python company with experts could use Natural Language Processing (NLP) in their programming, but with more programmers and developers learning Python, NLP is being used liberally in their daily programming. There are pre-written NLP libraries available that simplify the process and help the developers in focusing on building the models and fine-tuning the processes.
A Python company and its developers have access to multiple libraries that are available to all. If you are a Python company and you are interested in investing some time and resources into NLP, then here is a comparison of 5 most popular NLP libraries that can be highly beneficial for your business.
Natural Language Toolkit or NLTK
NLTK or Natural Language Toolkit is mostly used for programming tasks such as lemmatization, tokenization, parsing, stemming and more. It is one of the most popular NLP libraries in the market for its many advantages. It can be used with many third-party extensions, it opens avenues for many NLP tasks and makes them easier. Tokenization can be done easily in fast sentences with this NLP. Another reason why this is very popular is that it supports an incredibly large number of languages as compared to other NLP libraries. It is, however, a little difficult to learn or use and is quite slow.
spaCy is one of the main competitors of NLTK as both of these can be used for the same tasks and with the same processes. It is perhaps one of the fastest NLP frameworks to use, spaCy is one of the easiest to use libraries for any upcoming or experienced Python company. This library has a highly optimized tool for every task you can do from this library and makes sure that the programmer has complete control over the task at hand. This library uses neural networks for training quite a few of its models. It offers active support to the developers in real-time as well. It might not be as flexible as NLTK, but it is still preferred by many developers.
If your Python company has to work with text classification problems often then Scikit Learn is the best NLP library for you. It also offers a wide variety of algorithms that programmers and developers can use to build machine learning models and make their work easier. What really stands this NLP library apart is its ability to have good documentation methods and intuitive classes’ methods that make using this library easier. One drawback of this library to a Python company is that it does not use neutral networks for its text pre-processing, which can be a bit difficult to handle for new Python developers.
Gensim is an NLP package that is best used for vector space modelling, documentation and more. A Python company that often encounters datasets and data streams will find many advantages from this package. This NLP package easily works with these datasets and data stream processes so that the programmer can easily manage the task at hand and complete it quickly. It also supports deep learning and allows the programmer to use the package in multiple ways. However, one drawback of Gensim is that it can mostly be used for unsupervised text modelling and doesn’t have enough tools to offer full NLP functionality.
Pattern is basically a web mining module that supports NLP fully and irrevocably. This module supports web crawler, has API functionality and offers DOM parser, which makes it a good tool to be used by developers that have medium knowledge of using Python. Pattern also allows n-gram search WordNet, part-of-speech tagging, clustering, vector space model and SVM. However, as it is a web miner, it cannot be used to optimize many NLP tasks that you expect from an NLP library.
These are the five most popular libraries used by a Python company that wants to excel at Python programming. These libraries can be used for enhancing the process and make the application development process much easier for the developers.