TEITOK is a web-based platform for viewing, creating, and editing corpora with both rich textual mark-up and linguistic annotation, initially developed at the Centro de Linguística da Universidade de Lisboa, later at CELGA-ILTEC, and currently maintained at the ÚFAL institute of Charles University, Prague. The system has a modular design with numerous modules making serving a wide range of different corpus types. Below are some examples of some of those, and the type of corpora TEITOK can deal with. More modules are added frequently, and it is possible to add custom modules as well. The source is maintained at GitLab and some conversion tools are maintained on GitHub.

In this lesson you will first learn what topic modeling is and why you might want to employ it in your research. You will then learn how to install and work with the MALLET natural language proc...

MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. MALLET includes sophisticated tools for document classification: efficient routines for converting text to "features", a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics. In addition to classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields. These methods are implemented in an extensible system for finite state transducers. Topic models are useful for analyzing large collections of unlabeled text. The MALLET topic modeling toolkit contains efficient, sampling-based implementations of Latent Dirichlet Allocation, Pachinko Allocation, and Hierarchical LDA. Many of the algorithms in MALLET depend on numerical optimization. MALLET includes an efficient implementation of Limited Memory BFGS, among many other optimization methods. In addition to sophisticated Machine Learning applications, MALLET includes routines for transforming text documents into numerical representations that can then be processed efficiently. This process is implemented through a flexible system of "pipes", which handle distinct tasks such as tokenizing strings, removing stopwords, and converting sequences into count vectors. An add-on package to MALLET, called GRMM, contains support for inference in general graphical models, and training of CRFs with arbitrary graphical structure.

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. NLTK has been called “a wonderful tool for teaching, and working in, computational linguistics using Python,” and “an amazing library to play with natural language.” Natural Language Processing with Python provides a practical introduction to programming for language processing. Written by the creators of NLTK, it guides the reader through the fundamentals of writing Python programs, working with corpora, categorizing text, analyzing linguistic structure, and more. The online version of the book has been been updated for Python 3 and NLTK 3. (The original Python 2 version is still available at https://www.nltk.org/book_1ed.)