Text Processing Using NLTK in Python [Video]
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Free ChapterCorpus and WordNet
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Raw Text, Sourcing, and Normalization
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Pre-Processing
- Tokenization – Learning to Use the Inbuilt Tokenizers of NLTK
- Stemming – Learning to Use the Inbuilt Stemmers of NLTK
- Lemmatization – Learning to Use the WordNetLemmatizer of NLTK
- Stopwords – Learning to Use the Stopwords Corpus
- Edit Distance – Writing Your Own Algorithm to Find Edit Distance Between Two Strings
- Processing Two Short Stories and Extracting the Common Vocabulary
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Regular Expressions
- Regular Expression – Learning to Use *, +, and ?
- Regular Expression – Learning to Use Non-Start and Non-End of Word
- Searching Multiple Literal Strings and Substrings Occurrences
- Creating Date Regex
- Making Abbreviations
- Learning to Write Your Own Regex Tokenizer
- Learning to Write Your Own Regex Stemmer
Natural Language Processing (NLP) is a feature of Artificial Intelligence concerned with the interactions between computers and human (natural) languages. This course includes unique videos that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task.
In this course, you will learn what WordNet is and explore its features and usage. It will teach how to extract raw text from web sources and introduce some critical pre-processing steps. You will also get familiarized with the concept of pattern matching as a way to do text analysis.
By the end of the course, you will be confident & have covered various solutions, covering natural language understanding, Natural Language Processing, and syntactic analysis.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Text-Processing-using-NLTK-in-Python
Style and Approach
This video course takes a solution-based approach where every topic is explicated with the help of a real-world example.
- Publication date:
- April 2018
- Publisher
- Packt
- Duration
- 1 hour 24 minutes
- ISBN
- 9781789348989