Search Results for author: Dongyub Lee

Found 8 papers, 3 papers with code

Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis

no code implementations ACL 2021 Shinhyeok Oh, Dongyub Lee, Taesun Whang, IlNam Park, Gaeun Seo, EungGyun Kim, Harksoo Kim

In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i. e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies).

Aspect-Based Sentiment Analysis

Auxiliary Sequence Labeling Tasks for Disfluency Detection

no code implementations24 Oct 2020 Dongyub Lee, Byeongil Ko, Myeong Cheol Shin, Taesun Whang, Daniel Lee, Eun Hwa Kim, EungGyun Kim, Jaechoon Jo

Existing works for disfluency detection have focused on designing a single objective only for disfluency detection, while auxiliary objectives utilizing linguistic information of a word such as named entity or part-of-speech information can be effective.

Named Entity Recognition NER +2

Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization

no code implementations COLING 2020 Dongyub Lee, Myeongcheol Shin, Taesun Whang, Seungwoo Cho, Byeongil Ko, Daniel Lee, EungGyun Kim, Jaechoon Jo

In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS).

Text Summarization

EmotionX-KU: BERT-Max based Contextual Emotion Classifier

2 code implementations27 Jun 2019 Kisu Yang, Dongyub Lee, Taesun Whang, Seolhwa Lee, Heuiseok Lim

We propose a contextual emotion classifier based on a transferable language model and dynamic max pooling, which predicts the emotion of each utterance in a dialogue.

Emotion Recognition Language Modelling

Character-Level Feature Extraction with Densely Connected Networks

no code implementations COLING 2018 Chanhee Lee, Young-Bum Kim, Dongyub Lee, Heuiseok Lim

Generating character-level features is an important step for achieving good results in various natural language processing tasks.

Named Entity Recognition NER +2

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