Meganingrum Arista Jiwanggi


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A Multi-Pass Sieve Coreference Resolution for Indonesian
Valentina Kania Prameswara Artari | Rahmad Mahendra | Meganingrum Arista Jiwanggi | Adityo Anggraito | Indra Budi
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Coreference resolution is an NLP task to find out whether the set of referring expressions belong to the same concept in discourse. A multi-pass sieve is a deterministic coreference model that implements several layers of sieves, where each sieve takes a pair of correlated mentions from a collection of non-coherent mentions. The multi-pass sieve is based on the principle of high precision, followed by increased recall in each sieve. In this work, we examine the portability of the multi-pass sieve coreference resolution model to the Indonesian language. We conduct the experiment on 201 Wikipedia documents and the multi-pass sieve system yields 72.74% of MUC F-measure and 52.18% of BCUBED F-measure.


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Speech-Emotion Detection in an Indonesian Movie
Fahmi Fahmi | Meganingrum Arista Jiwanggi | Mirna Adriani
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

The growing demand to develop an automatic emotion recognition system for the Human-Computer Interaction field had pushed some research in speech emotion detection. Although it is growing, there is still little research about automatic speech emotion detection in Bahasa Indonesia. Another issue is the lack of standard corpus for this research area in Bahasa Indonesia. This study proposed several approaches to detect speech-emotion in the dialogs of an Indonesian movie by classifying them into 4 different emotion classes i.e. happiness, sadness, anger, and neutral. There are two different speech data representations used in this study i.e. statistical and temporal/sequence representations. This study used Artificial Neural Network (ANN), Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) variation, word embedding, and also the hybrid of three to perform the classification task. The best accuracies given by one-vs-rest scenario for each emotion class with speech-transcript pairs using hybrid of non-temporal and embedding approach are 1) happiness: 76.31%; 2) sadness: 86.46%; 3) anger: 82.14%; and 4) neutral: 68.51%. The multiclass classification resulted in 64.66% of precision, 66.79% of recall, and 64.83% of F1-score.