Posts about NLP

Text Normalization is an important part of preprocessing text for Natural Language Processing. There are several common techniques that we will go over in this post, using the Natural Language Toolkit (NLTK) in Python.

This is an overview of the COVID-19 Open Research Dataset (CORD-19), which is a corpus of research papers related to the coronavirus pandemic, and the Kaggle challenge to develop tools to process them using natural language processing techniques.

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.

When you want to label text data with named entities like people and location names, sometimes the out-of-the-box NER taggers do not quite meet your needs. Today we'll walk through the steps of training a Stanford NER model with a custom dataset.

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.

Text classification is a popular and important problem that we deal with on a daily basis. I will be creating a text classifier with Python and scikit-learn to filter a collection of articles based on whether or not they are fashion-related or not.