NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. Thanks to a hands-on guide introducing programming fundamentals alongside topics in computational linguistics, plus comprehensive API documentation, NLTK is suitable for linguists, engineers, students, educators, researchers, and industry users alike. NLTK is available for Windows, Mac OS X, and Linux. Best of all, NLTK is a free, open source, community-driven project.

This document describes a possible approach that can be used to check the relevance of a summary / definition of an entity with respect to its name. This classifier focuses on the relevancy of an entity's name to its summary / definition, in other words, it is a name relevance check. The percentage score obtained from this approach can be used either on its own or used to supplement scores obtained from other metrics to arrive upon a final classification; at the end of the document, potential improvements have also been outlined. The dataset that this document focuses on achieving an objective score is a list of package names and their respective summaries (sourced from pypi.org [1]).

Statsmodels is an open-source statistics-driven module that offers various classes and functions to the many statistical models available for statistical analysis and exploration of data. The module covers a vast number of models ranging from Linear Regression, Discrete Models, Time Series Analysis, Survival Analysis, and many other miscellaneous models.

Do you want to find out which are the best frameworks or libraries for natural language processing (NLP) in Python? Do you want to mine the social web and summarise blog posts? There are a lot of NLP libraries on the internet, but finding the right fit for your project is difficult. Natural Language Toolkit is one of the most popular platforms for building Python programs. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenisation, stemming, tagging, parsing, and semantic reasoning.

Are you interested in some practical natural language processing resources? There are so many NLP resources available online, especially those relying on deep learning approaches, that sifting through to find the quality can be quite a task. But what if you've completed these, have already gained a foundation in NLP and want to move to some practical resources, or simply have an interest in other approaches, which may not necessarily be dependent on neural networks? This post (hopefully) will be helpful. This is the introductory natural language processing book, at least from the dual perspectives of practicality and the Python ecosystem.

Orange is a component based data mining and machine learning software suite written in the Python language. It is an Open source data visualization and analysis for novice and experts. Data mining can be done through visual programming or Python scripting. It has components for machine learning. There are add ons for bioinformatics and text mining.