IBM Journey to AI blog

AUGUST 28, 2023

Text classification: Useful for tasks like sentiment classification, spam filtering and topic classification, text classification involves categorizing documents into predefined classes or categories. Using programming languages like Python with high-tech platforms like NLTK and SpaCy, companies can analyze user-generated content (e.g.,

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

DataRobot Blog

MARCH 9, 2022

It could be anything from a sentence to a paragraph to a longer-form collection of text. Analytically, we define the tf-idf of a term t as seen in document d , which is a member of a set of documents D as: tfidf( t, d, D ) = tf( t, d ) * idf( t, d, D ). Stanford – Reading Emotions From Speech Using Deep Neural Networks, a publication.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

Lexalytics

APRIL 5, 2021

I was out of the neural net biz. Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. The CNN was a 6-layer neural net with 132 convolution kernels and (don’t laugh!) Hinton (again!)

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52