Hi Douglas,
True, but also, in simple terms, the usage of "a" brings about Type
references oftentimes. Regardless of the missing "is" of "is a"
"a Norwegian"
could more formally be expressed as "a Norwegian type" or elongated to "a
Norwegian type person" or contextually "a Norwegian person".
Semantic Types (deduced or otherwise) are inherent in all languages studied.
How they are derived ... is often the maddening science that even the best
machine learning algorithms have not achieved fully with consistency.
In simple terms... We just need more clean data (examples in many
languages and ways of speaking and writing human languages).
But I'm confident we'll get there as a global society eventually through
Abstract Wikipedia, Wikipedia, Web Data Commons, IPFS, Filecoin, and other
data repositories as all of them continually expand and store our human
knowledge and ways of communicating our knowledge.
Thad
On Mon, Oct 25, 2021 at 11:33 AM Douglas Clark <clarkdd(a)gmail.com> wrote:
From the department of the self serving, I will note
that their focus for
the computational NLU micro theory is pragmatics.
They realized that humans shortcut their language use by not dealing with
the details but switching meanings. From the book:
“Regarding the latter, in his analysis of the place of formal semantics in
NLP, Wilks (2011) reports a thought-provoking finding about a sentence type
that has been discussed exten- sively in the theoretical literature,
illustrated by the well-known example John wants to marry a Norwegian. Such
sentences have been claimed to have two interpretations: John wants to
marry a particular Norwegian (de re), and he wants to marry some Norwegian
or other (de dicto). When Wilks carried out an informal web search for the
corresponding “wants to marry a German” (since marrying a Norwegian was
referenced in too many
linguistics papers), the first twenty hits all had the generic meaning,
which suggests that if one wants to express the specific meaning, this turn
of phrase is just not used. Wilks argues that computational semantics must
involve both meaning representation and “con- crete computational tasks on
a large scale” (p. 7). He writes, “What is not real Compsem [computational
semantics], even though it continues to masquerade under the name, is a
formal semantics based on artificial examples and never, ever, on real
computational and implemented processes” (p. 7).”
The “marry a German” research illustrates the need to reflect the real
world, but that we also paraphrase until we need to select a meaning that
the paraphrase meaning just can’t convey in context. Words don’t matter,
meaning does - see emojis. I submit that lexeme should define a concept
meaning not a word meaning. We use words, but communicate concepts. The NLU
computational construct must reflect the real use of language.
Further, for a knowledge representation, words and N-grams are not
knowledge carriers, concepts in context are the real world knowledge
carriers. We should probably model our NLU solution the same.
Tagged words and word proximity computing are a path that approximates how
we choose meaning, but fail in generating language due to the approximating
nature and lack of real context. We can, however, use that computational
approach to build a mirror of human communication (a pragmatic solution) by
vectorizing the real world use and de-duping (paraphrase grouping). The
result is also computational. Over time, we maintain meaning vectors and
paraphrase groups to better reflect real world human use. We de-emphasize
words and concentrate on concepts in contexts, just like human
communicators do.
Lastly, a pragmatic computational approach will have a natural language
interface - even non developers can read and interact with zero training -
just concepts and contexts with their associated knowledge items.
Thank you Amirouche, great read for NLP nerds.
On Sat, Oct 23, 2021 at 11:53 AM Amirouche BOUBEKKI <amirouche(a)hyper.dev>
wrote:
It is not my book, but I think it will interest
people around here. I
think it is an easy ready, it meant to be read by professionals and
hobbyist alike. There is no code.
A human-inspired, linguistically sophisticated model of language
understanding for intelligent agent systems.
The open access edition of this book was made possible by generous
funding from Arcadia – a charitable fund of Lisbet Rausing and Peter
Baldwin.
One of the original goals of artificial intelligence research was to
endow intelligent agents with human-level natural language capabilities.
Recent AI research, however, has focused on applying statistical and
machine learning approaches to big data rather than attempting to model
what people do and how they do it. In this book, Marjorie McShane and
Sergei Nirenburg return to the original goal of recreating human-level
intelligence in a machine. They present a human-inspired, linguistically
sophisticated model of language understanding for intelligent agent systems
that emphasizes meaning—the deep, context-sensitive meaning that a person
derives from spoken or written language.
With Linguistics for the Age of AI, McShane and Nirenburg offer a roadmap
for creating language-endowed intelligent agents (LEIAs) that can
understand,explain, and learn. They describe the language-understanding
capabilities of LEIAs from the perspectives of cognitive modeling and
system building, emphasizing “actionability”—which involves achieving
interpretations that are sufficiently deep, precise, and confident to
support reasoning about action. After detailing their microtheories for
topics such as semantic analysis, basic coreference, and situational
reasoning, McShane and Nirenburg turn to agent applications developed using
those microtheories and evaluations of a LEIA's language understanding
capabilities.
McShane and Nirenburg argue that the only way to achieve human-level
language understanding by machines is to place linguistics front and
center, using statistics and big data as contributing resources. They lay
out a long-term research program that addresses linguistics and real-world
reasoning together, within a comprehensive cognitive architecture.
https://direct.mit.edu/books/book/5042/Linguistics-for-the-Age-of-AI
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