Announcement of paper on learning syntactic categories.

[Steve Finch]

I have submitted a copy of a paper Nick Chater and I have written, to
the neuroprose archive. It details a hybrid system comprising a
statistically motivated network and a symbolic clustering mechanism
which together automatically classify words into a syntactic hierachy
by imposing a similarity metric over the contexts in which they are
observed to have occured in USENET newsgroup articles. The resulting
categories are very linguistically intuitive.

The abstract follows:

Symbolic and neural network architectures differ with respect to the
representations they naturally handle.  Typically, symbolic systems
use trees, DAGs, lists and so on, whereas networks typically use high
dimensional vector spaces.  Network learning methods may therefore
appear to be inappropriate in domains, such as natural language, which
are naturally modelled using symbolic methods. One reaction is to
argue that network methods are able to {\it implicitly} capture this
symbolic structure, thus obviating the need for explicit symbolic
representation.  However, we argue that the {\it explicit}
representation of symbolic structure is an important goal, and can be
learned using a hybrid approach, in which statistical structure
extracted by a network is transformed into a symbolic representation.
We apply this approach at several levels of linguistic structure,
using as input unlabelled orthographic, phonological and word-level
strings. We derive linguistically interesting categories such as
`noun', `verb', `preposition', and so on from unlabeled text.

To get it by anonymous ftp type

ftp archive.cis.ohio-state.edu

when asked for login name type anonymous; when asked for password type
neuron. 

Then type

cd pub/neuroprose
binary
get finch.hybrid.ps.Z
quit

Then uncompress it and lpr it.

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Steven Finch   Phone: +44 31 650 4435           | University of Edinburgh