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Natural Language Processing Fundamentals

You're reading from   Natural Language Processing Fundamentals Build intelligent applications that can interpret the human language to deliver impactful results

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789954043
Length 374 pages
Edition 1st Edition
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Authors (2):
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Dwight Gunning Dwight Gunning
Author Profile Icon Dwight Gunning
Dwight Gunning
Sohom Ghosh Sohom Ghosh
Author Profile Icon Sohom Ghosh
Sohom Ghosh
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Basic Feature Extraction Methods 3. Developing a Text classifier 4. Collecting Text Data from the Web 5. Topic Modeling 6. Text Summarization and Text Generation 7. Vector Representation 8. Sentiment Analysis Appendix

7. Vector Representation

Activity 12: Finding Similar Movie Lines Using Document Vectors

Solution

Let's build a movie search engine that finds similar movie lines to the one provided by the user. Follow these steps to complete this activity:

  1. Open a Jupyter notebook.
  2. Insert a new cell and add the following code to import all necessary libraries:
    import warnings
    warnings.filterwarnings("ignore")
    from gensim.models import Doc2Vec
    import pandas as pd
    from gensim.parsing.preprocessing import preprocess_string, remove_stopwords 
  3. Now we load the movie_lines1 file. After that, we need to iterate over each movie line in the file and split the columns. Also, we need to create a DataFrame containing the movie lines. Insert a new cell and add the following code to implement this:
    movie_lines_file = '../data/cornell-movie-dialogs/movie_lines1.txt'
    with open(movie_lines_file) as f:
        movie_lines = [line.strip().split('+++$...
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