Introducing spaCy

Kajol ajab   22 June,2019  

What is spaCy?

spaCy is a free, open-source Natural  Language Processing Library (NLP) in Python . Natural Language Processing (NLP) is one of the most interesting sub-fields of data science. For example,Natural language processing is widely used in sentiment analysis. It is  specifically intended for use in manufacturing and  helps  you create apps that process and  "understand" large volume  of text. 

spaCy is designed to help you do real work — to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive.spaCy is great at performing simple to complex text processing tasks. 

Hence is a quite fast library. spaCy provides a concise API to access its methods and properties governed by trained machine (and deep) learning models. It is designed particularly for production use, and it can help us to build applications that process massive volumes of text efficiently.


Why use spaCy?


Features of spaCy?


Feature Spacy NLTK Core NLP
Easy installation Y Y Y
Python API Y Y N
Multi Language support N Y Y
Tokenization Y Y Y
Part-of-speech tagging Y Y Y
Sentence segmentation Y Y Y
Dependency parsing Y N Y
Entity Recognition Y Y Y
Integrated word vectors Y N N
Sentiment analysis Y Y Y
Coreference resolution N N Y



Installation of spaCy


Image result for spacy nlp


Using pip, spaCy releases are available as source packages and binary wheels (as of v2.0.13).

pip install spacy                     

When using pip it is generally recommended to install packages in a virtual environment to avoid modifying system state:

python -m venv .env 

source .env/bin/activate    

pip install spacy                                                           


We’ll need to install spaCy and its English-language model before proceeding further.After installation you need to download a language model. 

python -m spacy download en_core_web_sm 

>>> import spacy
>>> nlp = spacy.load("en_core_web_sm")