基于论据边界识别的立场分类研究

doi:10.11896/jsjkx.210800180

摘要/Abstract

摘要： 在线辩论中作者通常会使用情绪化的语言来阐述自己的观点,表达方式较为随意。而论据作为对观点的佐证,则更多地包含了作者的立场倾向,因此论据信息对于立场识别有很大的帮助。基于BERT构建了论据边界识别模型,以提升在线辩论中文本立场分类的效果。首先利用BERT从辩论文本中抽取出与辩论主题相关的论据;然后将论据信息与文本信息进行融合,利用BERT模型得到辩论文本的立场分类结果。在英文在线辩论数据集上进行实验,与仅使用文本信息的BERT立场分类模型和其他机器学习模型相比,基于论据边界识别的模型在文本立场的分类效果上有更好的表现。

关键词: 论据识别, BERT, 立场分类

Abstract: In online debates,people often use emotional language to make their points with casual expressions.As a supporting evidence of these points,argument contains more of the author’s stance polarity,so the argument information is of great help to the stance detection.In this paper,we propose a stance detection model of BERT via argument boundary recognition to improve the effect of classification on texts in online debates.We use bidirectional encoder representations from transformers(BERT) to screen the arguments related to the topics from the debate posts.Then we combine the argument information and the text information to predict the stance polarity via BERT.Experiments are carried out on the English online debate dataset,compared with the stance analysis model of BERT using only text information and other machine learning models,the model based on argument boundary recognition has better performance in stance detection.

Key words: Argument recognition, BERT, Stance detection

中图分类号:

TP391

陈孜卓, 林夕, 王中卿. 基于论据边界识别的立场分类研究[J]. 计算机科学, 2022, 49(11A): 210800180-5. https://doi.org/10.11896/jsjkx.210800180

CHEN Zi-zhuo, LIN Xi, WANG Zhong-qing. Stance Detection Based on Argument Boundary Recognition[J]. Computer Science, 2022, 49(11A): 210800180-5. https://doi.org/10.11896/jsjkx.210800180

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