Archive for the ‘Context Models’ Category

Infinite Dimensional Word Embeddings [Variable Representation, Death to Triples]

Thursday, November 19th, 2015

Infinite Dimensional Word Embeddings by Eric Nalisnick and Sachin Ravi.

Abstract:

We describe a method for learning word embeddings with stochastic dimensionality. Our Infinite Skip-Gram (iSG) model specifies an energy-based joint distribution over a word vector, a context vector, and their dimensionality, which can be defined over a countably infinite domain by employing the same techniques used to make the Infinite Restricted Boltzmann Machine (Cote & Larochelle, 2015) tractable. We find that the distribution over embedding dimensionality for a given word is highly interpretable and leads to an elegant probabilistic mechanism for word sense induction. We show qualitatively and quantitatively that the iSG produces parameter-efficient representations that are robust to language’s inherent ambiguity.

Even better from the introduction:

To better capture the semantic variability of words, we propose a novel embedding method that produces vectors with stochastic dimensionality. By employing the same mathematical tools that allow the definition of an Infinite Restricted Boltzmann Machine (Côté & Larochelle, 2015), we describe ´a log-bilinear energy-based model–called the Infinite Skip-Gram (iSG) model–that defines a joint distribution over a word vector, a context vector, and their dimensionality, which has a countably infinite domain. During training, the iSGM allows word representations to grow naturally based on how well they can predict their context. This behavior enables the vectors of specific words to use few dimensions and the vectors of vague words to elongate as needed. Manual and experimental analysis reveals this dynamic representation elegantly captures specificity, polysemy, and homonymy without explicit definition of such concepts within the model. As far as we are aware, this is the first word embedding method that allows representation dimensionality to be variable and exhibit data-dependent growth.

Imagine a topic map model that “allow[ed] representation dimensionality to be variable and exhibit data-dependent growth.

Simple subjects, say the sort you find at schema.org, can have simple representations.

More complex subjects, say the notion of “person” in U.S. statutory law (no, I won’t attempt to list them here), can extend its dimensional representation as far as is necessary.

Of course in this case, the dimensions are learned from a corpus but I don’t see any barrier to the intentional creation of dimensions for subjects and/or a combined automatic/directed creation of dimensions.

Or as I put it in the title, Death to All Triples.

More precisely, not just triples but any pre-determined limit on representation.

Looking forward to taking a slow read on this article and those it cites. Very promising.

Understanding Context

Sunday, January 25th, 2015

Understanding Context by Andrew Hinton.

From the post:

Technology is destabilizing the way we understand our surroundings. From social identity to ubiquitous mobility, digital information keeps changing what here means, how to get there, and even who we are. Why does software so easily confound our perception and scramble meaning? And how can we make all this complexity still make sense to our users?

Understanding Context — written by Andrew Hinton of The Understanding Group — offers a powerful toolset for grasping and solving the challenges of contextual ambiguity. By starting with the foundation of how people perceive the world around them, it shows how users touch, navigate, and comprehend environments made of language and pixels, and how we can make those places better.

Understanding Context is ideal for information architects, user experience professionals, and designers of digital products and services of any scope. If what you create connects one context to another, you need this book.

Final_Final_forWeb_250_withMorville

Amazon summarizes in part:

You’ll discover not only how to design for a given context, but also how design participates in making context.

  • Learn how people perceive context when touching and navigating digital environments
  • See how labels, relationships, and rules work as building blocks for context
  • Find out how to make better sense of cross-channel, multi-device products or services
  • Discover how language creates infrastructure in organizations, software, and the Internet of Things
  • Learn models for figuring out the contextual angles of any user experience

This book is definitely going on my birthday wish list at Amazon. (There done!)

Looking forward to a slow read and in the meantime, will start looking for items from the bibliography.

My question, of course, is that after expending all the effort to discover and/or design a context, how do I pass that context onto another?

To someone coming from a slightly different context? (Assuming always that the designer is “in” a context.)

From a topic map perspective, what subjects do I need to represent to capture a visual context? Even more difficult, what properties of those subjects do I need to capture to enable their discovery by others? Or to facilitate mapping those subjects to another context/domain?

Definitely a volume I would assign as reading for a course on topic maps.

I first saw this in a tweet by subjectcentric.

Sarcastic Computers?

Thursday, May 31st, 2012

You may have seen the headline: Could Sarcastic Computers Be in Our Future? New Math Model Can Help Computers Understand Inference.

And the lead for the article sounds promising:

In a new paper, the researchers describe a mathematical model they created that helps predict pragmatic reasoning and may eventually lead to the manufacture of machines that can better understand inference, context and social rules.

Language is so much more than a string of words. To understand what someone means, you need context.

Consider the phrase, “Man on first.” It doesn’t make much sense unless you’re at a baseball game. Or imagine a sign outside a children’s boutique that reads, “Baby sale — One week only!” You easily infer from the situation that the store isn’t selling babies but advertising bargains on gear for them.

Present these widely quoted scenarios to a computer, however, and there would likely be a communication breakdown. Computers aren’t very good at pragmatics — how language is used in social situations.

But a pair of Stanford psychologists has taken the first steps toward changing that.

Context being one of those things you can use semantic mapping techniques to capture, I was interested.

Jack Park pointed me to a public PDF of the article: Predicting pragmatic reasoning in language games

Be sure to read the entire file.

A blue square, a blue circle, a green square.

Not exactly a general model for context and inference.

Context models and out-of-context objects

Saturday, May 5th, 2012

Context models and out-of-context objects by Myung Jin Choia, Antonio Torralbab, Alan S. Willskyc.

Abstract:

The context of an image encapsulates rich information about how natural scenes and objects are related to each other. Such contextual information has the potential to enable a coherent understanding of natural scenes and images. However, context models have been evaluated mostly based on the improvement of object recognition performance even though it is only one of many ways to exploit contextual information. In this paper, we present a new scene understanding problem for evaluating and applying context models. We are interested in finding scenes and objects that are “out-of-context”. Detecting “out-of-context” objects and scenes is challenging because context violations can be detected only if the relationships between objects are carefully and precisely modeled. To address this problem, we evaluate different sources of context information, and present a graphical model that combines these sources. We show that physical support relationships between objects can provide useful contextual information for both object recognition and out-of-context detection.

The authors distinguish object recognition in surveillance video versus still photographs, the subject of the investigation here. A “snapshot” if you will.

Subjects in digital media, assuming you don’t have the authoring data stream, exist in “snapshots” of a sort don’t they?

To start with they are bound up in a digital artifact, which among other things lives in a file system, with a last modified date, amongst many other files.

There may be more “context” for subjects in digital files that appears at first blush. Will have to give that some thought.