4 research outputs found
A Dependency Annotation Scheme to Extract Syntactic Features in Indonesian Sentences
In languages with
fixed word orders, syntactic information is useful when solving natural
language processing (NLP) problems. In languages like Indonesian, however,
which has a relatively free word order, the usefulness of syntactic information
has yet to be determined. In this study, a dependency annotation scheme for
extracting syntactic features from a sentence is proposed. This annotation
scheme adapts the Stanford typed dependency (SD) annotation scheme to cope with
such phenomena in the Indonesian language as ellipses, clitics, and non-verb
clauses. Later, this adapted annotation scheme is extended in response to the
inability to avoid certain ambiguities in assigning heads and relations. The
accuracy of these two annotation schemes are then compared, and the usefulness
of the extended annotation scheme is assessed using the syntactic features
extracted from dependency-annotated sentences in a preposition error correction
task. The experimental results indicate that the extended annotation scheme
improved the accuracy of a dependency parser, and the error correction task
demonstrates that training data using syntactic features obtain better
correction than training data that do not use such features, thus lending a
positive answer to the research question