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Jeongwoo Ko

Jeongwoo Ko

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    GoEmotions: A Dataset of Fine-Grained Emotions
    Dorottya Demszky
    Alan Cowen
    Gaurav Nemade
    Sujith Ravi
    ACL (2020) (to appear)
    Preview abstract Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement. View details
    Analysis of an Expert Search Query Log
    Yi Fang
    Naveen Somasundaram
    Luo Si
    Aditya P. Mathur
    SIGIR (2011)
    Probabilistic Models for Answer Ranking in Multilingual Question Answering
    Luo Si
    Eric Nyberg
    Teruko Mitamura
    Transactions on Information Systems (2010)
    The JAVELIN Question-Answering System at TREC 2002
    Eric Nyberg
    Teruko Mitamura
    Jaime G. Carbonell
    James P. Callan
    Kevyn Collins-Thompson
    Michael Duggan
    Laurie Hiyakumoto
    N. Hu
    Yifen Huang
    Lucian Vlad Lita
    S. Murtagh
    Vasco Pedro
    David Svoboda
    TREC (2002)