What is Topic Modeling? Topic modelling, in the context of Natural Language Processing, is described as a method of uncovering hidden structure in a collection of texts. Although that is indeed true it is also a pretty useless definition. Let’s define topic modeling in more practical terms.
The most direct definition of the task is: “Does a text express a positive or negative sentiment?”. Usually, we assign a polarity value to a text. This value is usually in the [-1, 1] interval, 1 being very positive, -1 very negative.
In a previous article, we studied training a NER (Named-Entity-Recognition) system from the ground up, using the Groningen Meaning Bank Corpus. This article is a continuation of that tutorial. The main purpose of this extension to training a NER is to: Replace the classifier with a Scikit-Learn Classifier Train a NER on a larger subset […]
Part-Of-Speech tagging (or POS tagging, for short) is one of the main components of almost any NLP analysis. The task of POS-tagging simply implies labelling words with their appropriate Part-Of-Speech (Noun, Verb, Adjective, Adverb, Pronoun, …).
Simple recipe for text clustering. This sometimes creates issues in scikit-learn because text has sparse features.
Text classification is most probably, the most encountered Natural Language Processing task. It can be described as assigning texts to an appropriate bucket. A sports article should go in SPORT_NEWS, and a medical prescription should go in MEDICAL_PRESCRIPTIONS. To train a text classifier, we need some annotated data. This training data can be obtained through […]