Open Domain Suggestion Mining: Problem Definition and Datasets
원문 초록
We propose a formal definition for the task of suggestion mining in the context of a wide range of open domain applications. Human perception of the term \emph{suggestion} is subjective and this effects the preparation of hand labeled datasets for the task of suggestion mining. Existing work either lacks a formal problem definition and annotation procedure, or provides domain and application specific definitions. Moreover, many previously used manually labeled datasets remain proprietary. We first present an annotation study, and based on our observations propose a formal task definition and annotation procedure for creating benchmark datasets for suggestion mining. With this study, we also provide publicly available labeled datasets for suggestion mining in multiple domains.
함께 살펴볼 논문
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YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis
Suggestion Mining from Online Reviews using ULMFiT
NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining
Aspect Oriented Suggestion Extraction from Online Reviews
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet
Open Domain Suggestion Mining Leveraging Fine-Grained Analysis
토론
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