Book Image

Natural Language Processing Fundamentals

By : Sohom Ghosh, Dwight Gunning
Book Image

Natural Language Processing Fundamentals

By: Sohom Ghosh, Dwight Gunning

Overview of this book

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
Table of Contents (10 chapters)

7. Vector Representation

Activity 12: Finding Similar Movie Lines Using Document Vectors


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
    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('+++$...