Book Image

Natural Language Processing Fundamentals

By : Sohom Ghosh, Dwight Gunning
Book Image

Natural Language Processing Fundamentals

By: Sohom Ghosh, Dwight Gunning

Overview of this book

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
Table of Contents (10 chapters)

Feature Engineering

Feature engineering is a method for extracting new features from existing features. These new features are extracted as they tend to effectively explain variability in data. One application of feature engineering could be to calculate how similar different pieces of text are. There are various ways of calculating the similarity between two texts. The most popular methods are cosine similarity and Jaccard similarity. Let's learn about each of them:

  • Cosine similarity: The cosine similarity between two texts is the cosine of the angle between their vector representations. BoW and TF-IDF matrices can be regarded as vector representations of texts.
  • Jaccard similarity: This is the ratio of the number of terms common between two text documents to the total number of unique terms present in those texts.

    Let's understand this with the help of an example. Suppose there are two texts:

    Text 1: I like detective Byomkesh Bakshi.

    Text 2: Byomkesh Bakshi is not...