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Deep Neural Network-Based Automated Essay Trait Scoring Model Incorporating Argument Structure Information
http://doi.org/10.5626/JOK.2023.50.8.662
Automated essay scoring is the task of having a model read a given essay and evaluate it automatically. This paper presents a method for automated essay scoring by creating essay representations that reflect argument structure of the essay using Argument Mining, and learning essay representations for each trait score. Results of our experiments indicated that the proposed essay representation outperformed representations obtained from pre-trained language models. Furthermore, it was found that learning different representations for each evaluation criterion was more effective for essay evaluation. The performance of the proposed model, as measured by the Quadratic Weighted Kappa (QWK) metric, improved from 0.543 to 0.627, showing a high level of agreement with human evaluations. Qualitative evaluations also showed that the proposed model demonstrated similar evaluation tendencies to human evaluations.
Analyzing the Effects of API Calls in Android Malware Detection Using Machine Learning
Seonghyun Park, Munyeong Kang, Jihyeon Park, Seong-je Cho, Sangchul Han
http://doi.org/10.5626/JOK.2021.48.3.257
This paper evaluates the effect of preprocessing and representing API call information on the accuracy of the system to detect malicious Android apps. We extract API calls that access or control sensitive data from target apps, and use the calls in machine learning algorithms to detect malicious apps. We then determine which expression of the API calls is most effective in classifying the apps as malicious or benign. Four ways of representing each API call are considered: class/method name with and without arguments/return type, and its presence (whether an API is called or not) and its frequency if called. The detection system has performed slightly better when the arguments/return type and the frequency of each API call were considered together. Its feature size was most efficient when considering the class/method name and the presence of each API call.
An Analysis of Linear Argumentation Structure of Korean Debate Texts Using Sequential Modeling and Linguistic Features
http://doi.org/10.5626/JOK.2018.45.12.1292
Current studies on argument mining provide tree-structured argumentation structures based on relational nuclearities and discourse relations between sentences in each document. In this case, inconsistencies between related sentences may occur, constructing a full argumentation structure for a document by the bottom-up method. This paper introduces relations between the topic of texts and sentences to provide a frame of argumentation structure. Automatic analysis of argumentation structure uses contextual information from documents, as argument types defined for each sentence are applied to the sequential model. In this paper, we vectorized sentences using bag-of-words of morphemes, word embedding of morphemes, and some linguistic features extracted from the sentence respectively, and used those vectors as inputs of models to predict argument types in the document. As a result, the combination of linguistic features and the sequential model revealed the best result in the experiment, showing 0.68 as the f1-score.
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