Semi-automatic Acquisition of Two-Level Morphological Rules for Iban Language

Abstract

Abstract. We describe in this paper a semi-automatic acquisition of morphological rules for morphological analyser in the case of underresourced language, which is Iban language. We modify ideas from previous automatic morphological rules acquisition approaches, where the input requirements has become constraints to develop the analyser for under-resourced language. This work introduces three main steps in acquiring the rules from the under-resourced language, which are morphological data acquisition, morphological information validation and morphological rules extraction. The experiment shows that this approach gives successful results with 0.76 of precision and 0.99 of recall. Our findings also suggest that the availability of linguistic references and the selection of assorted techniques for morphology analysis could lead to the design of the workflow. We believe this workflow will assist other researchers to build morphological analyser with the validated morphological rules for the under-resourced languages.

Discovering linguistic knowledge by converting printed dictionaries of minority languages into machine readable dictionaries

“…However, the robustness values are high. Reference [12] used induced morphological information from Linguistica to generate semiautomatically rules to be used by an Iban two-level morphological system. The approach taken is very promising in creating morphological system for underresourced and minority languages as it shows 0.76 of precision and 0.69 recall.…”