Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval

• URL: http://arxiv.org/abs/2211.10991v1
• Date: Sun, 20 Nov 2022 14:37:53 GMT
• Title: Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
• Authors: Taiqiang Wu, Xingyu Bai, Weigang Guo, Weijie Liu, Siheng Li, Yujiu Yang
• Abstract summary: We propose GER to capture more fine-grained information as complementary to sentence embeddings. We learn the fine-grained information about mention/entity by aggregating information from these knowledge units. Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models.
• Score: 11.533614615010643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue that such coarse-grained sentence embeddings can not fully model the mentions/entities, especially when the attention scores towards mentions/entities are relatively low. In this work, we propose GER, a \textbf{G}raph enhanced \textbf{E}ntity \textbf{R}etrieval framework, to capture more fine-grained information as complementary to sentence embeddings. We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph. Hence, we can learn the fine-grained information about mention/entity by aggregating information from these knowledge units. To avoid the graph information bottleneck for the central mention/entity node, we construct a hierarchical graph and design a novel Hierarchical Graph Attention Network~(HGAN). Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models. The code has been available at https://github.com/wutaiqiang/GER-WSDM2023.

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