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Iterative Disambiguation

In a prior post we showed how extraordinarily ambiguous, long sentences can be precisely interpreted. Here we take a simpler look upon request.

Let’s take a sentence that has more than 10 parses and configure the software to disambiguate among no more than 10.

Once again, this is a trivial sentence to disambiguate in seconds without iterative parsing!

The immediate results might present:

Suppose the intent is not that the telescope is with my friend, so veto “telescope with my friend” with a right-click.

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Parsing Winograd Challenges

The Winograd Challenge is an alternative to the Turing Test for assessing artificial intelligence.  The essence of the test involves resolving pronouns.  To date, systems have not fared well on the test for several reasons.  There are 3 that come to mind:

  1. The natural language processing involved in the word problems is beyond the state of the art.
  2. Resolving many of the pronouns requires more common sense knowledge than state of the art systems possess.
  3. Resolving many of the problems requires pragmatic reasoning beyond the state of the art.

As an example, one of the simpler exemplary problems is:

  • There is a pillar between me and the stage, and I can’t see around it.

A heuristic system (or a deep learning one) could infer that “it” does not refer to “me” or “I” and toss a coin between “pillar” and “stage”.  A system worthy of the passing the Winograd Challenge should “know” it’s the pillar.

Even this simple sentence presents some NLP challenges that are easy to overlook.  For example, does “between” modify the pillar or the verb “is”?

This is not much of a challenge, however, so let’s touch on some deeper issues and a more challenging problem…

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Background for our Semantic Technology 2013 presentation

In the spring of 2012, Vulcan engaged Automata for a knowledge acquisition (KA) experiment.  This article provides background on the context of that experiment and what the results portend for artificial intelligence applications, especially in the areas of education.  Vulcan presented some of the award-winning work referenced here at an AI conference, including a demonstration of the electronic textbook discussed below.  There is a video of that presentation here.  The introductory remarks are interesting but not pertinent to this article.

Background on Vulcan’s Project Halo

Background on Vulcan's Project Halo

From 2002 to 2004, Vulcan developed a Halo Pilot that could correctly answer between 30% and 50% of the questions on advanced placement (AP) tests in chemistry.  The approaches relied on sophisticated approaches to formal knowledge representation and expert knowledge engineering.  Of three teams, Cycorp fared the worst and SRI fared the best in this competition.  SRI’s system performed at the level of scoring a 3 on the AP, which corresponds to earning course credit at many universities.  The consensus view at that time was that achieving a score of 4 on the AP was feasible with limited additional effort.  However, the cost per page for this level of performance was roughly $10,000, which needed to be reduced significantly before Vulcan’s objective of a Digital Aristotle could be considered viable.

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