Emergent Computation for Semantic Interpretation by Rule-Based Equivalent Transformation
Hiroshi Mabuchi, Kiyoshi Akama, Hidekatsu Koike
In Proc. of the International Workshop on Functional and (Constraint) Logic Programming (WFLP 2001)
, Report No. 2017, University of Kiel
Abstract
The meaning of a sentence must be interpreted properly by using not
only the given sentence, but also factors such as the situation in
which the sentence was uttered and background knowledge in the domain
of the topic.
One of the problems when an algorithm, in which the computation order
is fixed, is used is the need to assume a variety of cases and
describe a number of separate algorithms that are suitable for each
case. In order to separate cases properly, the situation in which a
sentence was uttered and background knowledge must be considered in
combination. However, since the number of possible combinations is
extremely large, it is difficult to correctly describe them.
Moreover, the maintenance required due to the addition of situations
and the revision of background knowledge is costly.
Another problem is the efficiency of the computation. In natural
language understanding, since a number of different kinds of
constraints are related complicatedly, selection and flexible
processing of an appropriate constraint are required at each stage of
constraint elimination in order to handle the constraints efficiently.
When attempting to execute this processing using an algorithm in which
the computation order is fixed, an appropriate constraint that should
be solved at each stage can not be selected from among the whole
possible constraints, and the volume of unnecessary computations
becomes very large.
In order to solve these problems, we propose a new method for semantic
interpretation. In this method, creation of individual algorithms
written to handle various situations is avoided, and the basic
knowledge in the object domain and the situation in which a sentence
was uttered as well as the given sentence are described as a
declarative program. The semantic interpretation is then performed by
equivalent transformation using correct rules. With this method,
efficient processing matched to changing conditions emerges naturally
in the computation process.
The framework of the computation is referred to as Rule-Based
Equivalent Transformation (RBET), which is based on a new model called
the declarative computation model. In RBET, a given
problem is formalized as a problem which transforms one declarative
program into another declarative program. The program is successively
transformed into different programs by selecting and applying an
appropriate rule from a number of equivalent transformation (ET)
rules. The answer can be obtained from the final version of the
program.
RBET does not fix the computation order beforehand and aims to achieve
flexible and efficient computation by selecting the rule to apply
using controls, such as minimizing the number of clauses.