Parsing Indonesian Sentence into Abstract Meaning Representation using Machine Learning Approach
This work addresses a domain-specific gap in AMR parsing for Indonesian, but it is incremental as it builds on existing approaches with no major innovations.
The paper tackled the problem of parsing Indonesian sentences into Abstract Meaning Representation (AMR), which is limited in prior research, by developing a machine learning system based on existing methods, achieving a SMATCH score of 0.820 on simple sentence test data.
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.