Tianxing Wu
Abstract
This paper studies entity linking (EL) in Web tables, which aims to link the string mentions in table cells to their referent entities in a knowledge base. Two main problems exist in previous studies: 1) contextual information is not well utilized in mention-entity similarity computation; 2) the assumption on entity coherence that all entities in the same row or column are highly related to each other is not always correct. In this paper, we propose NPEL, a new Neural Paired Entity Linking framework, to overcome the above problems. In NPEL, we design a deep learning model with different neural networks and an attention mechanism, to model different kinds of contextual information of mentions and entities, for mention-entity similarity computation in Web tables. NPEL also relaxes the above assumption on entity coherence by a new paired entity linking algorithm, which iteratively selects two mentions with the highest confidence for EL. Experiments on real-world datasets exhibit that NPEL has the best performance compared with state-of-the-art baselines in different evaluation metrics.
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Index Terms
- NPEL: Neural Paired Entity Linking in Web Tables
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