Neural Entity Linking on Technical Service Tickets

• URL: http://arxiv.org/abs/2005.07604v2
• Date: Tue, 19 May 2020 14:11:16 GMT
• Title: Neural Entity Linking on Technical Service Tickets
• Authors: Nadja Kurz, Felix Hamann, Adrian Ulges
• Abstract summary: We show that a neural approach outperforms and complements hand-coded entities, with improvements of about 20% top-1 accuracy. We also show that a simple sentence-wise encoding (Bi-Encoder) offers a fast yet efficient search in practice.
• Score: 1.3621712165154805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity linking, the task of mapping textual mentions to known entities, has recently been tackled using contextualized neural networks. We address the question whether these results -- reported for large, high-quality datasets such as Wikipedia -- transfer to practical business use cases, where labels are scarce, text is low-quality, and terminology is highly domain-specific. Using an entity linking model based on BERT, a popular transformer network in natural language processing, we show that a neural approach outperforms and complements hand-coded heuristics, with improvements of about 20% top-1 accuracy. Also, the benefits of transfer learning on a large corpus are demonstrated, while fine-tuning proves difficult. Finally, we compare different BERT-based architectures and show that a simple sentence-wise encoding (Bi-Encoder) offers a fast yet efficient search in practice.

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