Book Image

The Natural Language Processing Workshop

By : Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning
5 (1)
Book Image

The Natural Language Processing Workshop

5 (1)
By: Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning

Overview of this book

Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.
Table of Contents (10 chapters)
Preface

Key Input Parameters for LSA Topic Modeling

We will be using the gensim library to perform LSA topic modeling. The key input parameters for gensim are corpus, the number of topics, and id2word. Here, the corpus is specified in the form of a list of documents in which each document is a list of tokens. The id2word parameter refers to a dictionary that is used to convert the corpus from a textual representation to a numeric representation such that each word corresponds to a unique number. Let's do an exercise to understand this concept better.

spaCy is a popular natural language processing Library for Python. In our exercises, we will be using spaCy to tokenize the text, lemmatize the tokens, and check which part-of-speech that token is. We will be using spaCy v2.1.3. After installing spaCy v2.1.3 we will need to download the English language model using the following code, so that we can load this model (since there are models for many different languages).

python -m spacy...