The following script creates a simple spaCy document. A document can be a sentence or a group of sentences and can have unlimited length. Let's now create a small document using this model.
In the script above we use the load function from the spacy library to load the core English language model.
Next, we need to load the spaCy language model. The following command downloads the language model: $ python -m spacy download enīefore we dive deeper into different spaCy functions, let's briefly see how to work with it.Īs a first step, you need to import the spacy library as follows: import spacy The language model is used to perform a variety of NLP tasks, which we will see in a later section. We will be using the English language model. Once you download and install spaCy, the next step is to download the language model. Otherwise if you are using Anaconda, you need to execute the following command on the Anaconda prompt: $ conda install -c conda-forge spacy If you use the pip installer to install your Python libraries, go to the command line and execute the following statement: $ pip install -U spacy However, we will also touch NLTK when it is easier to perform a task using NLTK rather than spaCy. In this series of articles on NLP, we will mostly be dealing with spaCy, owing to its state of the art nature. NLTK was released back in 2001 while spaCy is relatively new and was developed in 2015. The basic difference between the two libraries is the fact that NLTK contains a wide variety of algorithms to solve one problem whereas spaCy contains only one, but the best algorithm to solve a problem. The spaCy library is one of the most popular NLP libraries along with NLTK. In this article, we will start working with the spaCy library to perform a few more basic NLP tasks such as tokenization, stemming and lemmatization. We saw how to read and write text and PDF files. In the previous article, we started our discussion about how to do natural language processing with Python.
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