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利用描述信息和上下文理解增强实体链接
首发时间：2024-06-06
摘要：实体链接是将文本中的实体与知识库中对应实体进行关联的任务，在知识抽取、问答推理和信息检索等任务中非常重要。传统方法通常只考虑实体表面形式或知识图谱结构来完成实体链接。在本论文中，我们不仅考虑这些因素，还充分利用实体描述信息。实体描述提供实体的背景信息，可以增强实体嵌入的语义信息。此外，我们利用大语言模型出色的上下文学习能力进一步优化实体链接结果。我们设计了一个两阶段的实体链接算法。第一阶段结合实体的结构和描述信息，利用平移距离模型和注意力机制为实体编码进行初步召回。第二阶段利用大语言模型的上下文学习能力选择最终的实体链接结果。通过在ccks2019实体链接数据集上的实验评估，我们的方法超越了传统未充分利用实体描述信息的算法。这表明我们提出的实体链接算法在实践中具有较高的效果和应用潜力，并为进一步研究和应用实体链接提供了指导。
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enhancing entity linking through utilizing description information and context understanding
abstract：entity linking is the task of associating entities mentioned in text with their corresponding entities in a knowledge base. it plays a crucial role in tasks such as knowledge extraction, question-answering reasoning, and information retrieval. traditional methods often rely solely on the surface form of entities or the structure of the knowledge graph to accomplish entity linking. however, in this paper, we not only consider these factors but also fully utilize entity description information. entity descriptions provide contextual information about entities, enhancing the semantic information of entity embeddings. furthermore, we leverage the remarkable context-learning capabilities of large language models to further optimize the entity linking results. we have designed a two-stage entity linking algorithm. in the first stage, we combine the structural and descriptive information of entities, utilizing a translational distance model and attention mechanisms to encode entities for initial recall. in the second stage, we harness the context-learning abilities of large language models to select the final entity linking results. through experimental evaluation on the ccks2019 entity linking dataset, our method outperforms traditional algorithms that fail to fully leverage entity description information. this demonstrates that our proposed entity linking algorithm exhibits high effectiveness and application potential in practical scenarios, providing valuable guidance for further research and applications in entity linking.
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