BCCWords: Bayesian Text Sentiment Analysis using Crowdsourced Annotations

How do we build an automated tool for text sentiment analysis that learns from crowdsourced human annotations? This is the challenge addressed by the Bayesian Classifier Combination with Words (BCCWords) model presented in the paper:  Edwin Simpson,  Matteo Venanzi,  Steven Reece,  Pushmeet Kohli,  John Guiver,  Stephen Roberts  and  Nicholas R. Jennings, (2015)  Language Understanding in the Wild: Combining Crowdsourcing and Machine Learning. …

1

Community-Based Bayesian Classifier Combination

  In this post, we’re going to discuss how to use Infer.NET to implement the Community-based Bayesian Classifier Combination (ComminityBCC) model for aggregating crowd-sourced labels described in the paper:   Matteo Venanzi, John Guiver, Gabriella Kazai, Pushmeet Kohli, and Milad Shokouhi, Community-Based Bayesian Aggregation Models for Crowdsourcing, in Proceedings of the 23rd International World Wide…

2

Causal inference with Infer.NET

Update: The paper Causality with Gates is now available which describes the theory behind this blog post. An oft quoted phrase is “correlation does not imply causation”.  It means that if A tends to be true when B is true (i.e. A and B are correlated), then it is not correct to assume that A causes B…

1

Bayesian PCA

This blog has been migrated from community.research.microsoft.com Original date of blog: February 3, 2009 Original blog author: John Guiver NOTE: This example and the corresponding code are now directly available in the Infer.NET release. It’s been a couple of months now since we’ve released Infer.NET to the outside world, and a blog is overdue. I…

0

New features in Infer.NET 2.4: the softmax factor

This blog has been migrated from community.research.microsoft.com Original date of blog: November 24, 2010 Original blog author: David Knowles The new release of Infer.NET supports a number of new factors you may find useful when designing your models. Some of these features have been well tested, while others are still experimental and should be used with…

2

The separation of model and inference

This blog has been migrated from community.research.microsoft.com Original date of blog: November 18, 2010 Original blog author: John Guiver This is the second in a series of blogs about the Infer.NET 2.4 Beta 1 release, and highlights the benefits of an important design principle of Infer.NET – the separation of model and inference. The Infer.NET…

0

Performance improvements in Infer.NET 2.4

This blog has been migrated from community.research.microsoft.com Original date of blog: November 8, 2010 Original blog author: John Winn Hello Infernauts!! Now that version 2.4 of Infer.NET is released, we’re planning a series of blog posts to describe the new features and capabilities that we’ve added over the last 12 months.    The main focus of…

0

Calibrating reviews of conference submissions

This post has been migrated from community.research.microsoft.com. Original date of blog: February 1, 2010 Original blog author: John Guiver In this post, we’re going to look at how to use Infer.NET to streamline the conference reviewing process. The process in a typical computer science conference involves each submission being reviewed by several reviewers of differing…

0