Summary of Overview Of the Vlsp 2023 — Comom Shared Task: a Data Challenge For Comparative Opinion Mining From Vietnamese Product Reviews, by Hoang-quynh Le et al.
Overview of the VLSP 2023 – ComOM Shared Task: A Data Challenge for Comparative Opinion Mining from Vietnamese Product Reviews
by Hoang-Quynh Le, Duy-Cat Can, Khanh-Vinh Nguyen, Mai-Vu Tran
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an overview of the Comparative Opinion Mining from Vietnamese Product Reviews shared task, which aims to advance natural language processing by developing techniques for extracting comparative opinions from Vietnamese product reviews. The task requires participants to propose models that extract a “quintuple” (Subject, Object, Aspect, Predicate, and Comparison Type Label) from comparative sentences. A human-annotated dataset is constructed, comprising 120 documents with 7427 non-comparative sentences and 2468 comparisons within 1798 sentences. Models are evaluated and ranked based on the Exact match macro-averaged quintuple F1 score. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a special task that helps computers understand opinions in Vietnamese product reviews. The goal is to find important information like what’s being compared, what’s being compared to, and why they’re different or similar. To do this, researchers need to create better models that can extract the right details from sentences. A big dataset with many examples was created to test these models. |
Keywords
* Artificial intelligence * F1 score * Natural language processing
