Self-training vs Pre-trained Embeddings for Automatic Essay Scoring

Zhou, Xianbing; Yang, Liang; Fan, Xiaochao; Ren, Guanghui; Yang, Yong; Lin, Hongfei

doi:10.1007/978-3-030-88189-4_12

Cited by 2 publications

(1 citation statement)

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“…Although these four training scenarios were trained in a larger number of iterations, the accuracy of the model was not better than the model that used selftrained word embedding. This fact is in line with the results of research [35] where self-training embedding has a better performance than pretrained embedding.…”

Section: Classification Resultssupporting

confidence: 91%

Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings

Agustiningsih

Utami

Alsyaibani

2022

Jurnal Ilmu Komputer dan Informasi

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Sentiment analysis regarding the COVID-19 vaccine can be obtained from social media because users usually express their opinions through social media. One of the social media that is most often used by Indonesian people to express their opinion is Twitter. The method used in this research is Bidirectional LSTM which will be combined with word embedding. In this study, fastText and GloVe were tested as word embedding. We created 8 test scenarios to inspect performance of the word embeddings, using both pre-trained and self-trained word embedding vectors. Dataset gathered from Twitter was prepared as stemmed dataset and unstemmed dataset. The highest accuracy from GloVe scenario group was generated by model which used self-trained GloVe and trained on unstemmed dataset. The accuracy reached 92.5%. On the other hand, the highest accuracy from fastText scenario group generated by model which used self-trained fastText and trained on stemmed dataset. The accuracy reached 92.3%. In other scenarios that used pre-trained embedding vector, the accuracy was quite lower than scenarios that used self-trained embedding vector, because the pre-trained embedding data was trained using the Wikipedia corpus which contains standard and well-structured language while the dataset used in this study came from Twitter which contains non-standard sentences. Even though the dataset was processed using stemming and slang words dictionary, the pre-trained embedding still can not recognize several words from our dataset.

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Section: Classification Resultssupporting

confidence: 91%