为了准确识别网络文本中的价值观倾向,该文提出了一种融合标签语义知识实现价值观多标签文本分类的策略。首先基于价值观理论体系,构建了价值观知识图谱;然后构建了价值观多标签文本分类数据集;最后提出了融合标签语义知识的价值观多标签文本分类模型,通过两种方式融合价值观标签的语义知识。其一,利用标签语义信息进行文本表示学习,获得每个标签对于文本中不同词的重要程度;其二,利用标签的语义知识,计算标签与文本的语义相似度,并与分类模型结果融合。实验表明,该方法可以较好地解决价值观多标签分类问题,尤其可以缓解“尾标签”问题,最终在top@1结果上达到62.44%的精确率,在top@3上达到66.92%的召回率。
Abstract
In order to identify value tendencies in web texts, this paper proposes a multi-label text classification strategy combining semantic knowledge of value labels. We first construct a value knowledge graph based on the value theory system, and collect a multi-label text classification value dataset. Then, we propose a multi-label text classification model which combines semantic knowledge of value labels in two ways. One is to use the semantic information of labels for text representation learning to obtain the importance of each label for different words in the text. The other is to use the semantic knowledge of labels to calculate the semantic similarities between labels and texts, and combine them with the classification results. Experiments results show that our method can solve the multi-label classification problem in the value domain, and can also alleviate the “tail-label” problem. The results achieve 62.44% precision on top@1 and 66.92% recall on top@3.
关键词
价值观 /
标签语义 /
知识图谱 /
多标签文本分类
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Key words
value /
label semantic /
knowledge graph /
multi-label text classification
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参考文献
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基金
传播内容认知国家重点实验室课题(A12002);国家自然科学基金(62176074);中央高校基本科研业务费专项资金(2022FRFK0600XX)
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