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“Only full page color ads can run on the back cover of the New York Times Magazine.”

A decade or so ago, we were debating how to educate Paul Allen’s artificial intelligence in a meeting at Vulcan headquarters in Seattle with researchers from IBM, Cycorp, SRI,  and other places.

We were talking about how to “engineer knowledge” from textbooks into formal systems like Cyc or Vulcan’s SILK inference engine (which we were developing at the time).   Although some progress had been made in prior years, the onus of acquiring knowledge using SRI’s Aura remained too high and the reasoning capabilities that resulted from Aura, which targeted University of Texas’ Knowledge Machine, were too limited to achieve Paul’s objective of a Digital Aristotle.  Unfortunately, this failure ultimately led to the end of Project Halo and the beginning of the Aristo project under Oren Etzioni’s leadership at the Allen Institute for Artificial Intelligence.

At that meeting, I brought up the idea of simply translating English into logic, as my former product called “Authorete” did.  (We renamed it before Haley Systems was acquired by Oracle, prior to the meeting.)

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“I don’t own a TV set. I would watch it.”

The following excerpt is from Hobbs, Jerry R. “Toward a useful concept of causality for lexical semantics.” Journal of Semantics 22.2 (2005): 181-209.

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Are vitamins subject to sales tax in California?

What is the part of speech of “subject” in the sentence:

  • Are vitamins subject to sales tax in California?

Related questions might include:

  • Does California subject vitamins to sales tax?
  • Does California sales tax apply to vitamins?
  • Does California tax vitamins?

Vitamins is the direct object of the verb in each of these sentences, so, perhaps you would think “subject” is a verb in the subject sentence…

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Common sense about deep learning

I regularly build deep learning models for natural language processing and today I gave one a try that has been the leader in the Stanford Question Answering Dataset (SQuAD).  This one is a impressive NLP platform built using PyTorch.  But it’s still missing the big picture (i.e., it doesn’t “know” much).

Generally,  NLP systems that emphasize Big Data (e.g., deep learning approaches) but eschew more explicit knowledge representation and reasoning are interesting but unintelligent.  Think Siri and Alexa, for example.  They might get a simple factoid question if a Google search can find closely related text, but not much more.

Here is a simple demonstration of problems that the state of the art in deep machine learning is far from solving…

Here is a paragraph from a Wall Street Journal article about the Fed today where the deep learning system has “found” what the pronouns “this” and “they” reference:

The essential point here is that the deep learning system is missing common sense.  It is “the need”, not “a raise” that is referenced by “this”.  And “they” references “officials”, not “the minutes”.

Bottom line: if you need your natural language understanding system to be smarter than this, you are not going to get there using deep learning alone.

Dictionary Knowledge Acquisition

The following is motivated by Section 6359 of the California Sales and Use Tax.  It demonstrates how knowledge can be acquired from dictionary definitions:

Here, we’ve taken a definition from WordNet and prefixed it with the word followed by a colon and parsed it using the Linguist.

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‘believed by many’

A Linguist user recently had a question about part of a sentence that boiled down to something like the following:

  • It is believed by many.

The question was whether “many” was an adjective, cardinality, or noun in this sentence.  It’s a reasonable question!

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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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Nominal semantics of ‘meaning’

Just a quick note about a natural language interpretation that came up for the following sentence:

  • Under that test, the rental to an oil well driller of a “rock bit” having an effective life of but one rental is a transaction in lieu of a transfer of title within the meaning of (a) of this section.

The NLP system comes up with many hundreds of plausible parses for this sentence (mostly because it’s considering lexical and syntactic possibilities that are not semantically plausible).  Among these is “meaning” as a nominalization.

From Wikipedia:

  • In linguistics, nominalization is the use of a word which is not a noun (e.g. a verb, an adjective or an adverb) as a noun, or as the head of a noun phrase, with or without morphological transformation.

It’s quite common to use the present participle of a verb as a noun.  In this case, Google comes up with this definition for the noun ‘meaning’:

  • what is meant by a word, text, concept, or action.

The NLP system has a definition of “meaning” as a mass or count noun as well as definitions for several senses of the verb “mean”, such as these:

  1. intend to convey, indicate, or refer to (a particular thing or notion); signify.
  2. intend (something) to occur or be the case.
  3. have as a consequence or result.

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TA/NLP: It’s a jungle out there!

Text analytics and natural language processing have made tremendous advances in the last few years.  Unfortunately, there is a lot more to understanding natural language that TA/NLP.

I was reading a paper today about NLP pipelines for question answering that used machine learning to find what tools are good at what tasks and to configure a pipeline by selecting the best tool for a given task from each of the types of components in the pipeline.  The paper has a long list of various components, so I checked a few out.  Most of those of interest were available on the web so that they could be easily composed into pipelines without a lot of software setup.  Looking at these I quickly tired in disappointment.  Here are some of the reasons.

I am not surprised by these results.  NLU is hard.  But they are not particularly strong results either.  I’m surprised that people find such results useful (if they do).

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Ending Back Pain with AI

A nationwide physical therapy business is renowned for eliminating chronic pain.  One unique aspect of this business is that it eliminates pain not by manipulation but by providing clients with expertly selected sequences of exercises that address problems in their functional anatomy.  In effect, the business helps people fix themselves and teaches them to maintain their musculoskeletal function for pain-free life.

The business has dozens of clinics with many more therapists.  Its expertise comes primarily from its founder and a number of long-time employees who have learned through experience.  We have been engaged to assist with several challenges on several occasions.

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