# Parsing Indonesian Sentence into Abstract Meaning Representation using Machine Learning Approach

arXiv: https://arxiv.org/abs/2103.03730 · HTML: https://mlsanity.com/paper/2103.03730/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [AMR Parsing as Sequence-to-Graph Transduction](https://mlsanity.com/paper/1905.08704/) (2019)
- [Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation](https://mlsanity.com/paper/1808.09160/) (2018)

## Built on by (most cited first, 1 of 1)

- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)

## Abstract (arXiv)

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.