PP-1999-13: Learning Shallow Context-Free Languages under Simple Distributions

PP-1999-13: Adriaans, Pieter W. (1999) Learning Shallow Context-Free Languages under Simple Distributions. [Report]

	Text (Full Text) PP-1999-13.text.ps.gz Download (83kB)
	Text (Abstract) PP-1999-13.abstract.txt Download (1kB)

Abstract

In this paper I present the EMILE 3.0 algorithm. It can learn shallow
context-free grammars efficiently. It does so under circumstances that,
from a perspective of complexity, come resonably close to the conditions
under which human beings learn a language. A language is shallow in its
descriptive length if all the relevant constructions we need to know to
learn it have a complexity that is logarithmic in the descriptive length
of the grammar as a whole. I claim that natural languages are shallow
and that shallowness in itself is an interesting general constraint in
the context of formal learning theory. I also introduce the concept of
separability. A language is separable if the validity of rules can be
tested by means of memberbership queries using simple substitutions.
Although I do not believe that human languages are strictly separable I
do think it is an important aspect of efficient human communication. I
illustrate the structure of the algorithm by means of an extensive
example, and I indicate how a careful analysis of the bias involved in
natural language learning may lead to versions of EMILE 3.0 that can be
implemented in real-life dialogue systems. I claim that the EMILE
approach could serve as a valuable metamodel for evaluating clustering
approaches to language learning. The results of EMILE are mainly
theoretical. They could potentially also be used to develop more
efficient variants of other approaches to language learning.

Item Type:	Report
Report Nr:	PP-1999-13
Series Name:	Prepublication (PP) Series
Year:	1999
Date Deposited:	12 Oct 2016 14:36
Last Modified:	12 Oct 2016 14:36
URI:	https://eprints.illc.uva.nl/id/eprint/13

Actions (login required)

View Item