most citedDeep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis

20 citations · 27 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2023

Transcending Traditional Boundaries: Leveraging Inter-Annotator Agreement (IAA) for Enhancing Data Management Operations (DMOps)

Damrin Kim, NamHyeok Kim, Chanjun Park +1

This paper presents a novel approach of leveraging Inter-Annotator Agreement (IAA), traditionally used for assessing labeling consistency, to optimize Data Management Operations (D…

cs.CL2021★ 5 cited

Improving Sentence-Level Relation Extraction through Curriculum Learning

Seongsik Park, Harksoo Kim

Sentence-level relation extraction mainly aims to classify the relation between two entities in a sentence. The sentence-level relation extraction corpus often contains data that a…

cs.CL2021★ 20 cited

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

Shinhyeok Oh, Dongyub Lee, Taesun Whang +4

Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these met…

cs.CL2021★ 2 cited

Grammar Accuracy Evaluation (GAE): Quantifiable Quantitative Evaluation of Machine Translation Models

Dojun Park, Youngjin Jang, Harksoo Kim

Natural Language Generation (NLG) refers to the operation of expressing the calculation results of a system in human language. Since the quality of generated sentences from an NLG…

cs.CL2021

Korean-English Machine Translation with Multiple Tokenization Strategy

Dojun Park, Youngjin Jang, Harksoo Kim

This work was conducted to find out how tokenization methods affect the training results of machine translation models. In this work, alphabet tokenization, morpheme tokenization,…