NLPinAI 2023 Abstracts


Area 1 - NLPinAI

Full Papers
Paper Nr: 6
Title:

Detection of Compound-Type Dark Jargons Using Similar Words

Authors:

Takuro Hada, Yuichi Sei, Yasuyuki Tahara and Akihiko Ohsuga

Abstract: Recently, drug trafficking on microblogs has increased and become a social problem. While cyber patrols are being conducted to combat such crimes, those who post messages that lead to crimes continue to communicate skillfully using so-called “dark jargon,” a term that conceals their criminal intentions, to avoid using keywords (“drug,” ”marijuana,” etc.) of the target of monitoring. Evading detection by the eyes of monitoring, they continue to communicate with each other skillfully. Even if the monitors learn these dark jargons, they become obsolete over time as they become more common, and new dark jargons emerge. We have proposed a method for detecting dark jargons with criminal intent based on differences in the usage of words in posts and have achieved a certain level of success. In this study, by using similar words, we propose a method for detecting compound-type dark jargons that combines two or more words, which have been difficult to detect using existing methods. To confirm the effectiveness of the proposed method, we conducted a detection experiment with compound words and a detection experiment with dark jargons. As a result, we confirmed that the proposed method enabled to detect compound-type dark jargons that could not be detected by existing methods.
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Paper Nr: 8
Title:

Natural Language Explanatory Arguments for Correct and Incorrect Diagnoses of Clinical Cases

Authors:

Santiago Marro, Benjamin Molinet, Elena Cabrio and Serena Villata

Abstract: The automatic generation of explanations to improve the transparency of machine predictions is a major challenge in Artificial Intelligence. Such explanations may also be effectively applied to other decision making processes where it is crucial to improve critical thinking in human beings. An example of that consists in the clinical cases proposed to medical residents together with a set of possible diseases to be diagnosed, where only one correct answer exists. The main goal is not to identify the correct answer, but to be able to explain why one is the correct answer and the others are not. In this paper, we propose a novel approach to generate argument-based natural language explanations for the correct and incorrect answers of standardized medical exams. By combining information extraction methods from heterogeneous medical knowledge bases, we propose an automatic approach where the symptoms relevant to the correct diagnosis are automatically extracted from the case, to build a natural language explanation. To do so, we annotated a new resource of 314 clinical cases, where 1843 different symptoms are identified. Results in retrieving and matching the relevant symptoms for the clinical cases to support the correct diagnosis and contrast incorrect ones outperform standard baselines.
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