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.

翻译：能够与人进行自然和基于知识的互动的谈话媒介建设需要理解用户的语句。实体链接(EL)是理解自然语言文本并将其与外部知识连接的有效和广泛使用的方法。但是,它表明,为说明文件而开发的现有EL方法对于对话来说并不理想,个人实体(例如“我的车”)和概念对于理解用户的语句至关重要。在本文件中,我们为对话中连接的实体引入了一个集合和一个工具。我们收集了1327个谈话语句的EL说明,其中包括与名称实体、概念和个人实体的链接。数据集用于培训我们连接对话实体的工具包,即CREL。不同于现有的EL方法,CREL是用来识别被命名实体和概念的。它还利用共同解决技术来识别个人实体和在对话中提及的明确实体。我们将CREL与最新技术进行比较,并显示它超越了所有现有的基线。