Normalisation of Indonesian-English Code-Mixed Text and its Effect on Emotion Classification

Yulianti, Evi; Kurnia, Ajmal; Adriani, Mirna; Duto, Yoppy Setyo

doi:10.14569/ijacsa.2021.0121177

Cited by 11 publications

(12 citation statements)

References 24 publications

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“…In general, DrQA pipeline combines bigram hashing with TF-IDF matching retriever and bidirectional RNN paragraph reader [13]. Some recent methods have utilized BERT for question answering, as a result of the effectiveness of BERT that has been shown in various text processing tasks in previous work [17], [18]. Alzubi et al [8] proposed COBERT, COVID-19 Question Answering System Using BERT.…”

Section: B Machine Reading Task For Question Answeringmentioning

confidence: 99%

Transfer Learning for Closed Domain Question Answering in COVID-19

Rachmawati¹,

Yulianti²

2022

IJACSA

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COVID-19 has been a popular issue around 2019 until today. Recently, there has been a lot of research being conducted to utilize a big amount of data discussing about COVID-19. In this work, we conduct a closed domain question answering (CDQA) task in COVID-19 using transfer learning technique. The transfer learning technique is adopted because a large benchmark for question answering about COVID-19 is still unavailable. Therefore, rich knowledge learned from a large benchmark of open domain QA are utilized using transfer learning to improve the performance of our CDQA system. We use retriever-reader framework for our CDQA system, and propose to use Sequential Dependence Model (SDM) as our retriever component to enhance the effectiveness of the system.Our result shows that the use of SDM retriever can improve the F-1 score of the state-of-the-art baseline CDQA system using BM25 and TF-IDF+cosine similarity retriever by 3,26% and 32,62%, respectively. The optimal parameter settings for our CDQA system are found to be as follows: using 20 top-ranked documents as the retriever's output, five sentences as the passage length, and BERT-Large-Uncased model as the reader. In this optimal parameter setting, SDM retriever can improve the F-1 score of the state-of-the-art baseline CDQA system using BM25 by 5,06 % and TF-IDF+cosine similarity retriever by 24,94 %. Our last experiment then confirms the merit of using transfer learning, since our best-performing model (double fine-tune SQuAD and COVID-QA) is shown to gain eight times higher accuracy than the baseline method without using transfer learning. Further fine-tuning the transfer learning model using closed domain dataset (COVID-QA) can increase the accuracy of the transfer learning model that only fine-tuning with SQuAD by 27, 26%.

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Section: B Machine Reading Task For Question Answeringmentioning

confidence: 99%

Transfer Learning for Closed Domain Question Answering in COVID-19

Rachmawati¹,

Yulianti²

2022

IJACSA

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“…This research only focuses on Indonesian-language tweets. Therefore, it cannot be concluded that the methods used in this research are also effective for tweets in other languages or in code-mixed languages [72,73]. Further studies are needed to test the effectiveness of the methods in other languages.…”

Section: Discussionmentioning

confidence: 92%

We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model

Simanjuntak

Mahendra

Yulianti

2022

BDCC

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Twitter user location data provide essential information that can be used for various purposes. However, user location is not easy to identify because many profiles omit this information, or users enter data that do not correspond to their actual locations. Several related works attempted to predict location on English-language tweets. In this study, we attempted to predict the location of Indonesian tweets. We utilized machine learning approaches, i.e., long-short term memory (LSTM) and bidirectional encoder representations from transformers (BERT) to infer Twitter users’ home locations using display name in profile, user description, and user tweets. By concatenating display name, description, and aggregated tweet, the model achieved the best accuracy of 0.77. The performance of the IndoBERT model outperformed several baseline models.

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“…Besides, exposure to English from social media and school makes Indonesians mix their languages with English ( Rizal & Stymne, 2020 ). As a result, mixing Indonesian, Javanese, and English in daily conversation becomes the most prevalent language combination in Indonesian societyt ( Yulianti et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…LID is critical for some subsequent natural language processing tasks in code-mixed documents ( Gundapu & Mamidi, 2018 ). Applying LID in the code-mixed text has become a foundation work of various NLP systems, including sentiment analysis ( Ansari & Govilkar, 2018 ; Mahata, Das & Bandyopadhyay, 2021 ), translation ( Barik, Mahendra & Adriani, 2019 ; Mahata et al, 2019 ), and emotion classification ( Yulianti et al, 2021 ). The absence of LID in pre-processing tasks can affect those NLP systems.…”

Section: Introductionmentioning

confidence: 99%

Corpus creation and language identification for code-mixed Indonesian-Javanese-English Tweets

Hidayatullah

Apong

Lai

et al. 2023

PeerJ Computer Science

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With the massive use of social media today, mixing between languages in social media text is prevalent. In linguistics, the phenomenon of mixing languages is known as code-mixing. The prevalence of code-mixing exposes various concerns and challenges in natural language processing (NLP), including language identification (LID) tasks. This study presents a word-level language identification model for code-mixed Indonesian, Javanese, and English tweets. First, we introduce a code-mixed corpus for Indonesian-Javanese-English language identification (IJELID). To ensure reliable dataset annotation, we provide full details of the data collection and annotation standards construction procedures. Some challenges encountered during corpus creation are also discussed in this paper. Then, we investigate several strategies for developing code-mixed language identification models, such as fine-tuning BERT, BLSTM-based, and CRF. Our results show that fine-tuned IndoBERTweet models can identify languages better than the other techniques. This is the result of BERT’s ability to understand each word’s context from the given text sequence. Finally, we show that sub-word language representation in BERT models can provide a reliable model for identifying languages in code-mixed texts.

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Normalisation of Indonesian-English Code-Mixed Text and its Effect on Emotion Classification

Cited by 11 publications

References 24 publications

Transfer Learning for Closed Domain Question Answering in COVID-19

Transfer Learning for Closed Domain Question Answering in COVID-19

We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model

Corpus creation and language identification for code-mixed Indonesian-Javanese-English Tweets

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