Word vectors are useful in NLP tasks to preserve the context or meaning of text data. In this post we will use Spacy to obtain word vectors, and transform the vectors into a feature matrix that can be used in a Scikit-learn pipeline.
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Coreference resolution is a task in Natural Language Processing that aims to group together all references to an entity, for example, a person like Rihanna, in text. In this post we use NeuralCoref - a Spacy extension - to do this in Python.
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In this post we will train a custom Named Entity Recognizer in Python with Spacy. I will go through the steps to prepare your data and train a model with it. Inspiration credit: text for the graphic is from Vogue magazine - link in post.
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Named Entity Recognition is a common task in Natural Language Processing that aims to label things like person or location names in text data. Today we will look at two examples in Python, using the popular libraries Stanford NLP and Spacy.
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