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Type affinity network for distantly supervised relation extraction
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AI
DOI：10.1016/j.neucom.2025.129684.png)
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Abstract
En 中文
Distantly Supervised Relation Extraction (DSRE) focuses on mining relational information between entities from text, which is a critical step in constructing and expanding knowledge graphs. However, this task typically faces the problem of long-tail distribution of relationships. Furthermore, much previous research has been limited to specific relational features, overlooking the potential discriminative information in the dependencies among these features. To address these issues, we propose a Type Affinity Network (TAN) that explicitly models the dependencies among relational features. Using differentiated entity-type information and local context information, we extract high-quality feature representations. The dynamic integration of type dependency information via a type affinity matrix effectively leverages potential discriminative information, enhancing relation extraction accuracy. Furthermore, aggregating implicit features of relationships forms base points as references, improving the ability of the model to handle long-tail relations. These relationship base points differ from traditional hierarchical structures, providing amore general method. We conduct extensive experiments on two benchmark DSRE datasets, namely NYT-10 and WIKI-20 m. Compared with multiple state-of-the-art DSRE methods, the TAN not only shows significant performance improvements in handling long-tail relations, but also demonstrates overall performance improvement.
Keywords:
Type affinity matrix
Distant supervision
Relation extraction
Base point
Long tail