LNN-EL：一种短文本实体链接的神经符号方法
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
摘要：
实体链接（EL），通过将文本中的提及链接到知识图谱中的实体来消除歧义的任务，对于文本理解，问答或会话系统至关重要。短文本上的实体链接（例如，单个句子或问题）由于上下文有限而提出了特别的挑战。虽然以前的方法使用的是逻辑或黑盒神经方法，但在这里，我们提出了LNN-EL，这是一种神经符号方法，它结合了使用基于一阶逻辑的可解释规则与神经学习性能的优点。尽管受限于使用规则，LNN-EL与SotA黑盒神经方法相比具有竞争力，并且具有可扩展性和可移植性的额外优势。特别是，我们表明，我们可以很容易地混合现有的规则模板由人类专家，多种类型的功能（先验，BERT编码，框嵌入等），甚至从以前的EL方法产生的分数，从而改善了这种方法。例如，在LC-QuAD-1.0数据集上，我们显示F1评分比之前的SotA增加了$4$ \%以上。最后，我们表明，使用逻辑提供的归纳偏差导致学习规则在数据集之间传输良好，即使没有微调，同时保持高精度。
英文摘要：
Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems. Entity linking on short text (e.g., single sentence or question) poses particular challenges due to limited context. While prior approaches use either heuristics or black-box neural methods, here we propose LNN-EL, a neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to using rules, LNN-EL performs competitively against SotA black-box neural approaches, with the added benefits of extensibility and transferability. In particular, we show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even scores resulting from previous EL methods, thus improving on such methods. For instance, on the LC-QuAD-1.0 dataset, we show more than $4$\% increase in F1 score over previous SotA. Finally, we show that the inductive bias offered by using logic results in learned rules that transfer well across datasets, even without fine tuning, while maintaining high accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
