Haryo Akbarianto Wibowo


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IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words
Haryo Akbarianto Wibowo | Made Nindyatama Nityasya | Afra Feyza Akyürek | Suci Fitriany | Alham Fikri Aji | Radityo Eko Prasojo | Derry Tanti Wijaya
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter
Alham Fikri Aji | Made Nindyatama Nityasya | Haryo Akbarianto Wibowo | Radityo Eko Prasojo | Tirana Fatyanosa
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This paper describes our team’s submission for the Social Media Mining for Health (SMM4H) 2021 shared task. We participated in three subtasks: Classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms. Our system is based on BERT model pre-trained on the domain-specific text. In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance. We achieved the first rank in both classifying adverse drug effects and COVID-19 self-report tasks.


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Cross-Lingual and Supervised Learning Approach for Indonesian Word Sense Disambiguation Task
Rahmad Mahendra | Heninggar Septiantri | Haryo Akbarianto Wibowo | Ruli Manurung | Mirna Adriani
Proceedings of the 9th Global Wordnet Conference

Ambiguity is a problem we frequently face in Natural Language Processing. Word Sense Disambiguation (WSD) is a task to determine the correct sense of an ambiguous word. However, research in WSD for Indonesian is still rare to find. The availability of English-Indonesian parallel corpora and WordNet for both languages can be used as training data for WSD by applying Cross-Lingual WSD method. This training data is used as an input to build a model using supervised machine learning algorithms. Our research also examines the use of Word Embedding features to build the WSD model.