Book Image

Python Natural Language Processing Cookbook

By : Zhenya Antić
Book Image

Python Natural Language Processing Cookbook

By: Zhenya Antić

Overview of this book

Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing.
Table of Contents (10 chapters)

Finding similar strings: the Levenshtein distance

When doing information extraction, in many cases we deal with misspellings, which can bring complications into the task. In order to get around this problem, several methods are available, including the Levenshtein distance. This algorithm finds the number of edits/additions/deletions needed to change one string into another. In this recipe, you will be able to use this technique to find a match for a misspelled email.

Getting ready

We will use the same packages and the dataset that we used in the previous recipe, and also the python-Levenshtein package, which can be installed using the following command:

pip install python-Levenshtein

How to do it…

We will read the dataset into a pandas DataFrame and use the emails extracted from it to search for a misspelled email.

Your steps should be formatted like so:

  1. Do the necessary imports:
    import pandas as pd
    import Levenshtein
    from Chapter05.regex import get_emails...