论文标题
链接技术服务票的神经实体
Neural Entity Linking on Technical Service Tickets
论文作者
论文摘要
链接的实体是将文本提及映射到已知实体的任务，最近已使用上下文化的神经网络解决了。我们解决了一个问题，这些结果是否为Wikipedia等大型高质量数据集进行了报告 - 转移到实用的业务用例中，标签稀缺，文本是低质量的，并且术语特定于领域。使用基于BERT的实体链接模型，BERT是自然语言处理中流行的变压器网络，我们表明，神经方法的表现优于手工编码的启发式方法，提高了20％的TOP-1准确性。同样，证明了在大型语料库上进行转移学习的好处，而微调很难。最后，我们比较了不同的基于BERT的体系结构，并表明一个简单的句子编码（BI-编码器）在实践中提供了快速而有效的搜索。
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.
