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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Combinatorial ambiguity? No problem!

Working on translating some legal documentations (sales and use tax laws and regulations) into compliance logic, we came across the following sentence (and many more that are even worse):

  • Any transfer of title or possession, exchange, or barter, conditional or otherwise, in any manner or by any means whatsoever, of tangible personal property for a consideration.

Natural language processing systems choke on sentences like this because of such sentences’ combinatorial ambiguity and NLP’s typical lack of knowledge about what can be conjoined or complement or modify what.

This sentences has many thousands of possible parses.  They involve what the scopes of each of the the ‘or’s are and what is modified by conditional, otherwise, or whatsoever and what is complemented by in, by, of, and for.

The following shows 2 parses remaining after we veto a number of mistakes and confirm some phrases from the 400 highest ranking parses (a few right or left clicks of the mouse):

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Simple, Fast, Effective, Active Learning

Recently, we “read” ten thousand recipes or so from a cooking web site.  The purpose of doing so was to produce a formal representation of those recipes for use in temporal reasoning by a robot.

Our task was to produce ontology by reading the recipes subject to conflicting goals.  On the one hand, the ontology was to be accurate so that the robot could reason, plan, and answer questions robustly.  On the other hand, the ontology was to be produced automatically (with minimal human effort).[1]

In order to minimize human effort while still obtaining deep parses from which we produce ontology, we used more techniques from statistical natural language processing than we typically do in knowledge acquisition for deep QA, compliance, or policy automation.  (Consider that NLP typically achieves less than 90% syntactic accuracy while such work demands near 100% semantic accuracy.)[2]

In the effort, we refined some prior work on representing words as vectors and semi-supervised learning.  In particular, we adapted semi-supervised, active learning similar to Stratos & Collins 2015 using enhancements to the canonical correlation analysis (CCA) of Dhillon et al 2015 to obtain accurate part of speech tagging, as conveyed in the following graphic from Stratos & Collins:

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Of Kalman Filters and Hidden Markov Models

This provides some background relating to some work we did on part of speech tagging for a modest, domain-specific corpus.  The path is from Hsu et al 2012, which discusses spectral methods based on singular value decomposition (SVD) as a better method for learning hidden Markov models (HMM) and the use of word vectors instead of clustering to improve aspects of NLP, such as part of speech tagging.

The use of vector representations of words for machine learning in natural language is not all that new.  A quarter century ago, Brown et al 1992 introduced hierarchical agglomerative clustering of words based on mutual information and the use of those clusters within hidden Markov language models.  One notable difference versus today’s word vectors is that paths through the hierarchy of clusters to words at the leaves correspond to vectors of bits (i.e., Boolean features) rather than real-valued features.

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Artificially Intelligent Physical Therapy

Over the last 25 years we have developed two generations of AI systems for physical therapy.  The first was before the emergence of the Internet, when Windows was 16 bits.  There were no digital cameras, either.  So, physical therapists would take Polaroid pictures; anterior and left and right lateral pictures or simply eyeball a patient and enter their posture into the computer using a diagram like the following:

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Impressive result from Google

This is pretty impressive work by Google!

They are seeing the objective behind the query.  It’s pretty simple, in theory, to see the verb “read” operating on the object “string” with source (i.e., “from) being consistent with an input stream (also handling the concatenated compound).

More impressive is that they have learned from such queries and content that people view following such queries, perhaps even more deeply, that character streams, scanners, and stream APIs are relevant.

And they have also narrow my results based on the frequency that I look at Java versus other implementation languages.

Robust Inference and Slacker Semantics

In preparing for some natural language generation[1], I came across some work on natural logic[2][3] and reasoning by textual entailment[4] (RTE) by Richard Bergmair in his PhD at Cambridge:

The work he describes overlaps our approach to robust inference from the deep, variable-precision semantics that result from linguistic analysis and disambiguation using the English Resource Grammar (ERG) and the Linguist™.

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It’s hard to reckon nice English

The title is in tribute to Raj Reddy‘s classic talk about how it’s hard to wreck a nice beach.

I came across interesting work on higher order and semantic dependency parsing today:

So I gave the software a try for the sentence I discussed in this post.  The results discussed below were somewhat disappointing but not unexpected.  So I tried a well know parser with similar results (also shown below).

There is no surprise here.  Both parsers are marvels of machine learning and natural language processing technology.  It’s just that understanding is far beyond the ken of even the best NLP.  This may be obvious to some, but many are surprised given all the hype about Google and Watson and artificial intelligence or “deep learning” recently.

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