Text Mining and Analytics: Pattern Matching & Information Extraction

Natural Language Processing    |    Intermediate
  • 12 videos | 1h 52m 15s
  • Includes Assessment
  • Earns a Badge
Rating 4.8 of 17 users Rating 4.8 of 17 users (17)
Sometimes, business wants to find similar-sounding words, specific word occurrences, and sentiment from the raw text. Having learned to extract foundational linguistic features from the text, the next objective is to learn the heuristic approach to extract non-foundational features which are subjective. In this course, learn how to extract synonyms and hypernyms with WordNet, a widely used tool from the Natural Language Toolkit (NLTK). Next, explore the regex module in Python to perform NLTK chunking and to extract specific required patterns. Finally, you will solve a real-world use case by finding sentiments of movies using WordNet. After comleting this course, you will be able to use a heuristic approach of natural language processing (NLP) and to illustrate the use of WordNet, NLTK chunking, regex, and SentiWordNet.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline the heuristic approach for natural language processing (nlp)
    Recall why wordnet is important
    Illustrate and extract synonyms and identify wordnet hierarchies - hypernyms and hyponyms
    Identify meronyms and holonyms
    Demonstrate the lexical resource for opinion mining and finding the sentiment of text
  • Demonstrate the python re module, re - search, find all, finditer, groups, find and replace, and split
    Demonstrate anchors, character classes, greedy, lazy and backtracking algorithms, and performance
    Perform basic information extraction using nltk chunking and regex rules
    Perform advanced information extraction using nltk chunking and regex rules
    Model and find sentiment of movie plots using sentiwordnet
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 34s
  • 2m 27s
    Find out how to outline the heuristic approach for natural language processing (NLP). FREE ACCESS
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    3.  WordNet Fundamentals
    7m 21s
    After completing this video, you will be able to recall why WordNet is important. FREE ACCESS
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    4.  Performing Synonyms, Synset, and WordNet Hierarchy
    10m 28s
    Upon completion of this video, you will be able to illustrate and extract synonyms and identify WordNet hierarchies - hypernyms and hyponyms. FREE ACCESS
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    5.  Performing WordNet Relations and Semantic Similarity
    13m 4s
    To identify meronyms and holonyms, find out how they are related. Meronyms are words that describe a part of something, and holonyms are words that describe the whole of something. FREE ACCESS
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    6.  Working with SentiWordNet and Sentiment Analysis
    22m 6s
    In this video, find out how to apply the lexical resource for opinion mining and finding the sentiment of text. FREE ACCESS
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    7.  Working with Regex for Pattern Matching
    10m 35s
    In this video, you will learn how to apply the Python RE Module, including how to search, find all, finditer, groups, find and replace, and split. FREE ACCESS
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    8.  Investigating Python Regex Language
    16m 26s
    Learn about anchors, character classes, greedy, lazy, and backtracking algorithms, and performance. FREE ACCESS
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    9.  Performing Basic NLTK Chunking and Regex
    8m 39s
    In this video, you will perform basic information extraction using NLTK chunking and regex rules. FREE ACCESS
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    10.  Performing Advanced NLTK Chunking and Regex
    3m 44s
    Learn how to perform advanced information extraction using NLTK chunking and regular expression rules. FREE ACCESS
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    11.  Modeling Movie Plot Sentiment Analysis with WordNet
    14m 47s
    In this video, you will learn how to model and find sentiment in movie plots using SentiWordNet. FREE ACCESS
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    12.  Course Summary
    1m 3s

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