15 citations · 21 across the 8 of their papers we have counts for
6 papers · 1 filter
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie +9
Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG bench…
Multi-document Summarization using Semantic Role Labeling and Semantic Graph for Indonesian News Article
Yuly Haruka Berliana Gunawan, Masayu Leylia Khodra
In this paper, we proposed a multi-document summarization system using semantic role labeling (SRL) and semantic graph for Indonesian news articles. In order to improve existing su…
Fine-tuning Pretrained Multilingual BERT Model for Indonesian Aspect-based Sentiment Analysis
Annisa Nurul Azhar, Masayu Leylia Khodra
Although previous research on Aspect-based Sentiment Analysis (ABSA) for Indonesian reviews in hotel domain has been conducted using CNN and XGBoost, its model did not generalize w…
Parsing Indonesian Sentence into Abstract Meaning Representation using Machine Learning Approach
Adylan Roaffa Ilmy, Masayu Leylia Khodra
Abstract Meaning Representation (AMR) provides many information of a sentence such as semantic relations, coreferences, and named entity relation in one representation. However, re…
Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews
Jordhy Fernando, Masayu Leylia Khodra, Ali Akbar Septiandri
Aspect and opinion terms extraction from review texts is one of the key tasks in aspect-based sentiment analysis. In order to extract aspect and opinion terms for Indonesian hotel…
Handling Imbalanced Dataset in Multi-label Text Categorization using Bagging and Adaptive Boosting
Genta Indra Winata, Masayu Leylia Khodra
Imbalanced dataset is occurred due to uneven distribution of data available in the real world such as disposition of complaints on government offices in Bandung. Consequently, mult…