Building conversational agents that can have natural and knowledge-grounded
interactions with humans requires understanding user utterances. Entity Linking
(EL) is an effective and widely used method for understanding natural language
text and connecting it to external knowledge. It is, however, shown that
existing EL methods developed for annotating documents are suboptimal for
conversations, where personal entities (e.g., "my cars") and concepts are
essential for understanding user utterances. In this paper, we introduce a
collection and a tool for entity linking in conversations. We collect EL
annotations for 1327 conversational utterances, consisting of links to named
entities, concepts, and personal entities. The dataset is used for training our
toolkit for conversational entity linking, CREL. Unlike existing EL methods,
CREL is developed to identify both named entities and concepts. It also
utilizes coreference resolution techniques to identify personal entities and
references to the explicit entity mentions in the conversations. We compare
CREL with state-of-the-art techniques and show that it outperforms all existing
baselines.

本文介绍了一种对话实体链接的工具，使用了不同于现有方法的技术来识别如人称实体和命名实体这种在对话中扮演重要角色的实体，并在实验中证明了其优于已有技术的性能。