Natural Language Processing Made Easy – using SpaCy (in Python)
Introduction
Natural Language Processing is one of the principal areas of Artificial Intelligence. NLP plays a critical role in many intelligent applications such as automated chat bots, article summarizers, multi-lingual translation and opinion identification from data. Every industry which exploits NLP to make sense of unstructured text data, not just demands accuracy, but also swiftness in obtaining results.
Natural Language Processing is a capacious field, some of the tasks in nlp are – text classification, entity detection, machine translation, question answering, and concept identification. In one of my last article, I discussed various tools and components that are used in the implementation of NLP. Most of the components discussed in the article were described using venerated library – NLTK (Natural Language Toolkit).
In this article, I will share my notes on one of the powerful and advanced libraries used to implement nlp – spaCy.
Table of Content
- About spaCy and Installation
- SpaCy pipeline and properties
- Tokenization
- Pos Tagging
- Entity Detection
- Dependency