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A Visual Analytics System for Interpretable Machine Learning
http://doi.org/10.5626/JOK.2023.50.1.57
Interpretable machine learning is a technology that assists people understand the behavior and prediction of machine learning systems. This study proposes a visual analytics system that can interpret the relationship between how machine learning models relate output results from input data. It supports users to interpret machine learning models easily and clearly. The visual analytics system proposed in this study takes an approach to effectively interpret the machine learning model through an iterative adjustment procedure that filters and groups model decision results according to input variables, target variables, and predicted/classified values. Through use case analysis and in-depth user interviews, we confirmed that our system could provide insights into the complex behavior of machine learning models, gain scientific understanding of input variables, target variables, and model predictions, and help users understand the stability and reliability of models.
Grammatical Error Detection for L2 Learners Based on Attention Mechanism
Chanhee Park, Jinuk Park, Minsoo Cho, Sanghyun Park
http://doi.org/10.5626/JOK.2019.46.6.554
Grammar Error Detection refers to the work of discovering the presence and location of grammatical errors in a given sentence, and is considered to be useful for L2 learners to learn and evaluate the language. Systems for grammatical error correction have been actively studied, but there still exist limitations such as lack of training corpus and limited error type correction. Therefore, this paper proposes a model for generalized grammatical error detection through the sequence labeling problem which does not require the determination of error type. The proposed model dynamically decides character-level and word-level representation to deal with unexpected words in L2 learners" writing. Also, based on the proposed model the bias which can occur during the learning process with imbalanced data can be avoided through multi-task learning. Additionally, attention mechanism is applied to efficiently predict errors by concentrating on words for judging errors. To validate the proposed model, three test data were used and the effectiveness of the model was verified through the ablation experiment.
Biomedical Named Entity Recognition using Multi-head Attention with Highway Network
Minsoo Cho, Jinuk Park, Jihwan Ha, Chanhee Park, Sanghyun Park
http://doi.org/10.5626/JOK.2019.46.6.544
Biomedical named entity recognition(BioNER) is the process of extracting biomedical entities such as diseases, genes, proteins, and chemicals from biomedical literature. BioNER is an indispensable technique for the extraction of meaningful data from biomedical domains. The proposed model employs deep learning based Bi-LSTM-CRF model which eliminates the need for hand-crafted feature engineering. Additionally, the model contains multi-head attention to capture the relevance between words, which is used when predicting the label of each input token. Also, in the input embedding layer, the model integrates character-level embedding with word-level embedding and applies the combined word embedding into the highway network to adaptively carry each embedding to the input of the Bi-LSTM model. Two English biomedical benchmark datasets were employed in the present research to evaluate the level of performance. The proposed model resulted in higher f1-score compared to other previously studied models. The results demonstrate the effectiveness of the proposed methods in biomedical named entity recognition study.
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