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Oops, had meant to also send to the list.
Ali On Tue, Sep 27, 2011 at 2:15 PM, Ali SH <asaegyn+out@xxxxxxxxx> wrote:
Hi John,
Would be curious to hear your take on the following paper (link to preprint provided below, the accepted Computational Linguistics version comes out tomorrow). The author claims to provide a novel foundation for (computational) meaning, specifically in what he calls a "context-theoretic" framework (explicitly making the analogy to a model-theoretic framework):
Abstract
Techniques in which words are represented as vectors have proved useful in many applications in computational linguistics, however there is currently no general semantic formalism for representing meaning in terms of vectors. We present a framework for natural language semantics in which words, phrases and sentences are all represented as vectors, based on a theoretical analysis which assumes that meaning is determined by context. In the theoretical analysis, we define a corpus model as a mathematical abstraction of a text corpus. The meaning of a string of words is assumed to be a vector representing the contexts in which it occurs in the corpus model. Based on this assumption, we can show that the vector representations of words can be considered as elements of an algebra over a field. We note that in applications of vector spaces to representing meanings of words there is an underlying lattice structure; we interpret the partial ordering of the lattice as describing entailment between meanings. We also define the context-theoretic probability of a string, and, based on this and the lattice structure, a degree of entailment between strings.
We relate the framework to existing methods of composing vector-based representations of meaning, and show that our approach generalises many of these, including vector addition, component-wise multiplication, and the tensor product.
Best, Ali
On Mon, Sep 26, 2011 at 5:11 PM, John F. Sowa <sowa@xxxxxxxxxxx> wrote:
Topics in this forum have addressed both practical issues and highly
theoretical and philosophical issues. But the connections between
them are usually not very obvious.
I put together some slides from several recent talks, in which I
address both kinds of issues and try to relate them more clearly
than most discussions. (See the URL and outline below.)
Section #1 covers historical issues about ontology, artificial
intelligence, and the disappointing progress in developing universal
ontologies and computer systems that can "understand" language.
Sections #2, #3, and #4 cover theoretical issues about linguistics,
psychology, neuroscience, and philosophy.
Sections #5, #6, and #7 relate the theoretical and philosophical
issues to computational issues. They suggest ways of designing
and implementing computational systems that could have a better
chance of meeting the goals discussed in Section #1.
John
_____________________________________________________________________
Source: http://www.jfsowa.com/talks/ontofound.pdf
Foundations for Ontology
1. Problems and challenges
2. Psycholinguistics and neuroscience
3. Semantics of natural languages
4. Wittgenstein's early and later philosophy
5. Lattice of theories
6. Processing unrestricted natural language texts
7. Meeting the challenges
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