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 The Handbook of NLP with Gensim
  • Table Of Contents Toc
  • Feedback & Rating feedback
The Handbook of NLP with Gensim

The Handbook of NLP with Gensim

By : Chris Kuo
5 (6)
close
close
The Handbook of NLP with Gensim

The Handbook of NLP with Gensim

5 (6)
By: Chris Kuo

Overview of this book

Navigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios. You’ll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy. Next, you’ll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you’ll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications. By the end of this book, you’ll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.
Table of Contents (24 chapters)
close
close
1
Part 1: NLP Basics
5
Part 2: Latent Semantic Analysis/Latent Semantic Indexing
9
Part 3: Word2Vec and Doc2Vec
12
Part 4: Topic Modeling with Latent Dirichlet Allocation
18
Part 5: Comparison and Applications

PV-DBOW

Figure 8.2 is the neural network for PV-DBOW that has an input layer, a hidden layer, and an output layer. The input layer is a vector of the paragraph IDs. Assume a corpus has 500 paragraphs. Each of the paragraph IDs is one-hot encoded and the length of each paragraph vector is 500. For example, Paragraph “1” is a 1 x 500 vector where only the position of “1” is 1 and the rest are zeros.

Figure 8.2 – PV-DBOW

Figure 8.2 – PV-DBOW

Let’s see how a paragraph is prepared to feed into the neural network model.

The neural network requires data to follow the (input, output) format. Let’s first see how to do this. In Word2Vec, we organize texts into word pairs to feed into its neural network model. Its format is (word, adjacent word) for the input and output layer. In Doc2Vec, the format for the word pairs is (paragraph ID, word) for the input and output layer. Assume we have two paragraphs:

  • Paragraph 1: “...

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
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

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