Alfan Farizki Wicaksono


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Semantic Role Labeling in Conversational Chat using Deep Bi-Directional Long Short-Term Memory Networks with Attention Mechanism
Valdi Rachman | Rahmad Mahendra | Alfan Farizki Wicaksono | Ahmad Rizqi Meydiarso | Fariz Ikhwantri
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

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Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks
Ilham Fathy Saputra | Rahmad Mahendra | Alfan Farizki Wicaksono
Proceedings of the BioNLP 2018 workshop

We propose keyphrases extraction technique to extract important terms from the healthcare user-generated contents. We employ deep learning architecture, i.e. Long Short-Term Memory, and leverage word embeddings, medical concepts from a knowledge base, and linguistic components as our features. The proposed model achieves 61.37% F-1 score. Experimental results indicate that our proposed approach outperforms the baseline methods, i.e. RAKE and CRF, on the task of extracting keyphrases from Indonesian health forum posts.

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Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
Fariz Ikhwantri | Samuel Louvan | Kemal Kurniawan | Bagas Abisena | Valdi Rachman | Alfan Farizki Wicaksono | Rahmad Mahendra
Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP

Most Semantic Role Labeling (SRL) approaches are supervised methods which require a significant amount of annotated corpus, and the annotation requires linguistic expertise. In this paper, we propose a Multi-Task Active Learning framework for Semantic Role Labeling with Entity Recognition (ER) as the auxiliary task to alleviate the need for extensive data and use additional information from ER to help SRL. We evaluate our approach on Indonesian conversational dataset. Our experiments show that multi-task active learning can outperform single-task active learning method and standard multi-task learning. According to our results, active learning is more efficient by using 12% less of training data compared to passive learning in both single-task and multi-task setting. We also introduce a new dataset for SRL in Indonesian conversational domain to encourage further research in this area.


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Automatically Building a Corpus for Sentiment Analysis on Indonesian Tweets
Alfan Farizki Wicaksono | Clara Vania | Bayu Distiawan | Mirna Adriani
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing