Parsing Indonesian Sentence into Abstract Meaning Representation using Machine Learning Approach

TL;DR

This paper presents a machine learning system for parsing Indonesian sentences into Abstract Meaning Representation, achieving promising results on simple sentences, addressing the limited research in Indonesian AMR parsing.

Contribution

It introduces a novel Indonesian AMR parsing system based on a three-step machine learning approach, adapting existing methods to Indonesian language processing.

Findings

Achieved SMATCH score of 0.820 on simple sentences

Developed a three-step parsing system: pair prediction, label prediction, graph construction

Addresses the gap in Indonesian AMR research

Abstract

Abstract Meaning Representation (AMR) provides many information of a sentence such as semantic relations, coreferences, and named entity relation in one representation. However, research on AMR parsing for Indonesian sentence is fairly limited. In this paper, we develop a system that aims to parse an Indonesian sentence using a machine learning approach. Based on Zhang et al. work, our system consists of three steps: pair prediction, label prediction, and graph construction. Pair prediction uses dependency parsing component to get the edges between the words for the AMR. The result of pair prediction is passed to the label prediction process which used a supervised learning algorithm to predict the label between the edges of the AMR. We used simple sentence dataset that is gathered from articles and news article sentences. Our model achieved the SMATCH score of 0.820 for simple sentence test data.