Your First NLP Machine Learning Project: Perks and Pitfalls of Unstructured Data w/ Anna Widiger

anna-widigerAnna Widiger (@widiger_anna) has a B.A. degree in Computational Linguistics from University of Tübingen. She’s been doing NLP since her very first programming assignment, specializing in Russian morphology, German syntax, cross-lingual named entity recognition, topic modeling, and grammatical error detection.

Anna describes “Your First NLP Machine Learning Project: Perks and Pitfalls of Unstructured Data” to us. Faced with words instead of numbers, many data scientists prefer to feed words straight from csv files into lists without filtering or transformation, but there is a better way! Text normalization improves the quality of your data for future analysis and increases the accuracy of your machine learning model.

Which text preprocessing steps are necessary and which ones are “nice-to-have” depends on the source of your data and the information you want to extract from it. It’s important to know what goes into the bag of words and what metrics are useful to compare word frequencies in documents. In this hands-on talk, I will show some do’s and don’ts for processing tweets, Yelp reviews, and multilingual news articles using spaCy.