Book Image

Hands-On Natural Language Processing with Python

By : Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy
Book Image

Hands-On Natural Language Processing with Python

By: Rajesh Arumugam, Rajalingappaa Shanmugamani, Auguste Byiringiro, Chaitanya Joshi, Karthik Muthuswamy

Overview of this book

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.
Table of Contents (15 chapters)
6
Searching and DeDuplicating Using CNNs
7
Named Entity Recognition Using Character LSTM

Sentence2vec

One of the major drawbacks of the method that we discussed previously (for obtaining document-level vector representations) is that the words present in the document are given equal weight age. Such an approach suppresses the importance of certain words in the sentence, which actually add value to the meaning of the document. Let us discuss this point with a concrete example of how the method builds a document-level vector representation. Take a look at the sentence, Jack and Jill went up the hill to fetch a pail of water. In the sentence, the words that provide the most context about the action happening are Jack, Jill, hill, fetch, pail, and water. However, the preceding method builds the representation with equal weight given to every single word in the sentence.

An interesting approach that has been discussed is using the weighted average of word vectors, instead...