The processing of natural language is getting more and more popular. NLP is a field of artificial intelligence designed to understand and extract important information from text. The main areas that include the use of NLP are recognizing and generating voice, text analysis, sentiment analysis, automatic translation, etc.
There are various tools and libraries designed to resolve NLP problems. In this post we will look at some:
- NLTK (Natural Language Toolkit) is utilized for tokenization, lemmatization, teeming, parsing, POS labeling, etc. This library has almost all the tools for any NLP task.
- Spacy is the prime competitor of NLTK. These two libraries can be utilized for the same tasks.
- Scikit-learn offers a great library for machine learning. The tools for text processing also are available here.
- Gensim is the package to use for thematic spaces and vectors, also with text comparison.
- Pattern is a library of patterns to sere like a module for web mining. For this reason, it’s not so good for NLP but more of a secondary accessory.
- Polyglot is another Python package for NLP. This package is not that popular but it can be used for a variety of NLP tasks.
- TextBlob: TextBlob simplifies the processing of text providing an interface that is intuitively similar to NLTK. It poses an easy learning curve but it provides a great number of functions.
- Stanford CoreNLP: This package was developed by Stanford University for most, this library is one of the state-of-the-art techniques for Natural Language Processing. This library works with Java but it does have a Python library.
- pyLDAvis: This library is designed to help the users interpret the themes that come up in topic analysis. This lets us visualize in a simpler form the themes included in the text.