Related papers: GCRE-GPT: A Generative Model for Comparative Relation Extraction

GCRE-GPT: A Generative Model for Comparative Relation Extraction

URL: http://arxiv.org/abs/2303.08601v2
Date: Thu, 27 Jun 2024 06:34:11 GMT
Title: GCRE-GPT: A Generative Model for Comparative Relation Extraction
Authors: Yequan Wang, Hengran Zhang, Aixin Sun, Xuying Meng,
Abstract summary: Given comparative text, comparative relation extraction aims to extract two targets in comparison and the aspect they are compared for. Existing solutions formulate this task as a sequence labeling task, to extract targets and aspects. We show that comparative relations can be directly extracted with high accuracy, by generative model.
Score: 47.69464882382656
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Given comparative text, comparative relation extraction aims to extract two targets (\eg two cameras) in comparison and the aspect they are compared for (\eg image quality). The extracted comparative relations form the basis of further opinion analysis.Existing solutions formulate this task as a sequence labeling task, to extract targets and aspects. However, they cannot directly extract comparative relation(s) from text. In this paper, we show that comparative relations can be directly extracted with high accuracy, by generative model. Based on GPT-2, we propose a Generation-based Comparative Relation Extractor (GCRE-GPT). Experiment results show that \modelname achieves state-of-the-art accuracy on two datasets.

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