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Deeply integrating unsupervised semantics and syntax into heterogeneous graphs for inductive text classification.

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Titel: Deeply integrating unsupervised semantics and syntax into heterogeneous graphs for inductive text classification.
Autoren: Gao, Yue, Fu, Xiangling, Liu, Xien, Wu, Ji
Quelle: Complex & Intelligent Systems; Feb2024, Vol. 10 Issue 1, p1565-1579, 15p
Schlagwörter: SEMANTICS, KNOWLEDGE representation (Information theory), WEIGHTED graphs, SYNTAX (Grammar), NATURAL language processing, GRAPH algorithms, CLASSIFICATION
Firma/Körperschaft: UNITED States. Congress. Senate
Abstract: Graph-based neural networks and unsupervised pre-trained models are both cutting-edge text representation methods, given their outstanding ability to capture global information and contextualized information, respectively. However, both representation methods meet obstacles to further performance improvements. On one hand, graph-based neural networks lack knowledge orientation to guide textual interpretation during global information interaction. On the other hand, unsupervised pre-trained models imply rich semantic and syntactic knowledge which lacks sufficient induction and expression. Therefore, how to effectively integrate graph-based global information and unsupervised contextualized semantic and syntactic information to achieve better text representation is an important issue pending for solution. In this paper, we propose a representation method that deeply integrates Unsupervised Semantics and Syntax into heterogeneous Graphs (USS-Graph) for inductive text classification. By constructing a heterogeneous graph whose edges and nodes are totally generated by knowledge from unsupervised pre-trained models, USS-Graph can harmonize the two perspectives of information under a bidirectionally weighted graph structure and thereby realizing the intra-fusion of graph-based global information and unsupervised contextualized semantic and syntactic information. Based on USS-Graph, we also propose a series of optimization measures to further improve the knowledge integration and representation performance. Extensive experiments conducted on benchmark datasets show that USS-Graph consistently achieves state-of-the-art performances on inductive text classification tasks. Additionally, extended experiments are conducted to deeply analyze the characteristics of USS-Graph and the effectiveness of our proposed optimization measures for further knowledge integration and information complementation. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
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Abstract:Graph-based neural networks and unsupervised pre-trained models are both cutting-edge text representation methods, given their outstanding ability to capture global information and contextualized information, respectively. However, both representation methods meet obstacles to further performance improvements. On one hand, graph-based neural networks lack knowledge orientation to guide textual interpretation during global information interaction. On the other hand, unsupervised pre-trained models imply rich semantic and syntactic knowledge which lacks sufficient induction and expression. Therefore, how to effectively integrate graph-based global information and unsupervised contextualized semantic and syntactic information to achieve better text representation is an important issue pending for solution. In this paper, we propose a representation method that deeply integrates Unsupervised Semantics and Syntax into heterogeneous Graphs (USS-Graph) for inductive text classification. By constructing a heterogeneous graph whose edges and nodes are totally generated by knowledge from unsupervised pre-trained models, USS-Graph can harmonize the two perspectives of information under a bidirectionally weighted graph structure and thereby realizing the intra-fusion of graph-based global information and unsupervised contextualized semantic and syntactic information. Based on USS-Graph, we also propose a series of optimization measures to further improve the knowledge integration and representation performance. Extensive experiments conducted on benchmark datasets show that USS-Graph consistently achieves state-of-the-art performances on inductive text classification tasks. Additionally, extended experiments are conducted to deeply analyze the characteristics of USS-Graph and the effectiveness of our proposed optimization measures for further knowledge integration and information complementation. [ABSTRACT FROM AUTHOR]
ISSN:21994536
DOI:10.1007/s40747-023-01228-8