Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Python Natural Language Processing Cookbook
  • Table Of Contents Toc
  • Feedback & Rating feedback
Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

By : Zhenya Antić
4.4 (18)
close
close
Python Natural Language Processing Cookbook

Python Natural Language Processing Cookbook

4.4 (18)
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)
close
close

Training your own NER model with spaCy

The NER model provided by spaCy can suffice in many cases. There might be other times, however, when we would like to augment the existing model or create a new one from scratch. spaCy has a toolset specifically for that, and in this recipe, we will do both.

Getting ready

We will use the spacy package to train a new NER model. You do not need any other packages than spacy.

How to do it…

We will define our training data and then use it to update an existing model. We will then test the model and save it to disk. The code in this recipe is based on the spaCy documentation (https://spacy.io/usage/training#ner). The steps for this recipe are as follows:

  1. Import the necessary packages:
    import spacy
    from spacy.util import minibatch, compounding
    from spacy.language import Language
    import warnings
    import random
    from pathlib import Path
  2. Now we will define the training data that we will use:
    DATA = [
        ...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY