Exploring Computational Techniques with NLTK for Linguists
Dive into the world of computational techniques for linguists with NLTK in Lecture 24. Learn about list comprehensions, conditional forms, tokenization, part-of-speech tagging, parsing, chunking, concordance, similarity, common contexts, dispersion plots, and more. Discover where NLTK is installed o
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Exploring Sources, Tools, and Datasets in Text Mining
Discover a plethora of sources, tools, and datasets in text mining through resources shared by Bettina Berendt and references from lectures and publications. Uncover DH-specific tools and powerful NLP tools like Ling Pipe, OpenNLP, Stanford Parser, and NLTK Toolkit for text analysis and processing.
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Understanding Bigrams and Generating Random Text with NLTK
Today's lecture in the Computational Techniques for Linguists course covered the concept of bigrams using NLTK. Bigrams are pairs of words found in text, which are essential for tasks like random text generation. The lecture demonstrated how to work with bigrams, including examples from the NLTK boo
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