Overview of the VLSP 2023 -- ComOM Shared Task: A Data Challenge for Comparative Opinion Mining from Vietnamese Product Reviews

Hoang-Quynh Le, Duy-Cat Can, Khanh-Vinh Nguyen, Mai-Vu Tran
2/21/2024
cs.CLcs.LG

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Abstract

This paper presents a comprehensive overview of the Comparative Opinion Mining from Vietnamese Product Reviews shared task (ComOM), held as part of the 10$^{th}$ International Workshop on Vietnamese Language and Speech Processing (VLSP 2023). The primary objective of this shared task is to advance the field of natural language processing by developing techniques that proficiently extract comparative opinions from Vietnamese product reviews. Participants are challenged to propose models that adeptly extract a comparative "quintuple" from a comparative sentence, encompassing Subject, Object, Aspect, Predicate, and Comparison Type Label. We construct a human-annotated dataset comprising $120$ documents, encompassing $7427$ non-comparative sentences and $2468$ comparisons within $1798$ sentences. Participating models undergo evaluation and ranking based on the Exact match macro-averaged quintuple F1 score.

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Cite this paper

@article{le2024overview,
  title  = {Overview of the VLSP 2023 -- ComOM Shared Task: A Data Challenge for Comparative Opinion Mining from Vietnamese Product Reviews},
  author = {Hoang-Quynh Le and Duy-Cat Can and Khanh-Vinh Nguyen and Mai-Vu Tran},
  year   = {2024},
  eprint = {2402.13613},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.13613v2}
}

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