Soyoung Park


CILAB@FinTOC-2021 Shared Task: Title Detection and Table of Content Extraction for Financial Document
Hyuntae Kim | Soyoung Park | Seongeun Yang | Yuchul Jung
Proceedings of the 3rd Financial Narrative Processing Workshop


Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention
Daeun Lee | Soyoung Park | Jiwon Kang | Daejin Choi | Jinyoung Han
Findings of the Association for Computational Linguistics: EMNLP 2020

Early intervention for suicide risks with social media data has increasingly received great attention. Using a suicide dictionary created by mental health experts is one of the effective ways to detect suicidal ideation. However, little attention has been paid to validate whether and how the existing dictionaries for other languages (i.e., English and Chinese) can be used for predicting suicidal ideation for a low-resource language (i.e., Korean) where a knowledge-based suicide dictionary has not yet been developed. To this end, we propose a cross-lingual suicidal ideation detection model that can identify whether a given social media post includes suicidal ideation or not. To utilize the existing suicide dictionaries developed for other languages (i.e., English and Chinese) in word embedding, our model translates a post written in the target language (i.e., Korean) into English and Chinese, and then uses the separate suicidal-oriented word embeddings developed for English and Chinese, respectively. By applying an ensemble approach for different languages, the model achieves high accuracy, over 87%. We believe our model is useful in accessing suicidal ideation using social media data for preventing potential suicide risk in an early stage.