Search Results for author:
Found 16 papers, 6 papers with code
Suicidal Risk Detection for Military Personnel
We analyze social media for detecting the suicidal risk of military personnel, which is especially crucial for countries with compulsory military service such as the Republic of Korea.
Interpretable Convolutional SyncNet
Our model can better handle larger images, and its output can be given a probabilistic interpretation.
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Psychiatrists diagnose mental disorders via the linguistic use of patients.
Analyzing Norm Violations in Live-Stream Chat
Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter.
Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation
We show that the model trained with our datasets significantly outperforms the currently used statistical Korean GEC system (Hanspell) on a wider range of error types, demonstrating the diversity and usefulness of the datasets.
KOLD: Korean Offensive Language Dataset
Recent directions for offensive language detection are hierarchical modeling, identifying the type and the target of offensive language, and interpretability with offensive span annotation and prediction.
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles
This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
Fast End-to-end Coreference Resolution for Korean
Recently, end-to-end neural network-based approaches have shown significant improvements over traditional pipeline-based models in English coreference resolution.
Dimensional Emotion Detection from Categorical Emotion
We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations.
Additive Compositionality of Word Vectors
Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives.
Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues
A dataset of those interactions can be used to learn to automatically classify the client utterances into categories that help counselors in diagnosing client status and predicting counseling outcome.
Hierarchical Dirichlet Gaussian Marked Hawkes Process for Narrative Reconstruction in Continuous Time Domain
In news and discussions, many articles and posts are provided without their related previous articles or posts.
Subword-level Word Vector Representations for Korean
The results show that our simple method outperforms word2vec and character-level Skip-Grams on semantic and syntactic similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis.
Rotated Word Vector Representations and their Interpretability
We apply several rotation algorithms to the vector representation of words to improve the interpretability.
Understanding Editing Behaviors in Multilingual Wikipedia
We study multilingualism by collecting and analyzing a large dataset of the content written by multilingual editors of the English, German, and Spanish editions of Wikipedia.
