Abstract:
This article investigates the use of
transformation-based learning for resolving
function tagging ambiguity in the Myanmar
language. Function tagger plays an important
role in natural language applications like speech
recognition, natural language parsing,
information retrieval and information extraction.
In this paper, the function tagger [12] learns
rules to correct its mistakes. A set of rule
templates is used to create specific rules. At
initial stage of function tagging for Myanmar, it
is trained with a very limited resource of
annotated corpus. The performance can be
maximized with a substantial amount of
annotated corpus. The function tagset has been
developed for training and testing the function
tagger. The present tagset consists of 56 tags. A
corpus size of about three thousand sentences is
used for training and testing the accuracy of the
function tagger. The tagger learned 192 rules
(including lexical and contextual rules) and
achieved 93% accuracy.
