文献类型：期刊文献

英文题名：Entity understanding with hierarchical graph learning for enhanced text classification

作者：Wang, Chao[1];Jiang, Haiyun[3];Chen, Tao[1];Liu, Jingping[2];Wang, Menghui[1];Jiang, Sihang[1];Li, Zhixu[1];Xiao, Yanghua[1,4]

机构：[1]Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[3]Tencent AI Lab, Shenzhen, Peoples R China;[4]Fudan Aishu Cognit Intelligence Joint Res Ctr, Shanghai, Peoples R China

年份：2022

卷号：244

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20221311862946);WOS:【SCI-EXPANDED(收录号:WOS:000835835500014)】；

基金：We thank anonymous reviewers for their comments and suggestions. This work was supported by National Key Research and Development Project (No. 2020AAA0109302). Shanghai Science and Technology Innovation Action Plan (No. 19511120400). Shanghai Municipal Science and Technology Major Project (No. 2021SHZDZX0103).

语种：英文

外文关键词：Text classification; Hierarchical graph learning; Soft clustering

摘要：Text classification plays an important role in the areas of natural language processing and data mining. In general, a text is usually described around a collection of entities, i.e., the entities are the core part of the text. As a result, a deep understanding of the entities in a text benefits the classification of texts. To understand entities, traditional work tends to introduce concepts or web data for entities. However, we argue that the potential relations between entities are also important for the understanding of entity semantics, thus further supporting the classification of texts. In this paper, we focus on enhancing the performance of the existing text classification models by extracting features from entities with hierarchical graph learning. To this end, we mine the concepts of entities and the relations between them for a given text simultaneously, and further construct the semantic graph of the text. Then a novel hierarchical graph learning model is proposed to learn the graph embedding that well captures the node, relation, and graph structure information. Our experiments show that the proposed method has the ability to effectively improve the performance of the existing text classifiers. (c) 2022 Elsevier B.V. All rights reserved.