论文标题
个人实体，概念和命名实体在对话中链接
Personal Entity, Concept, and Named Entity Linking in Conversations
论文作者
论文摘要
建立可以与人类进行自然和知识互动的对话代理需要了解用户的话语。实体链接（EL）是一种有效且广泛使用的方法，用于理解自然语言文本并将其连接到外部知识。但是，这表明为注释文档开发的现有EL方法对于对话而言是次优的，其中个人实体（例如“我的汽车”）和概念对于理解用户的话语至关重要。在本文中，我们介绍了一个集合和一个用于对话中链接的实体的工具。我们收集1327次对话说法的EL注释，这些话语由指定实体，概念和个人实体的链接组成。该数据集用于培训我们的工具包，以链接对话实体链接，CREL。与现有的EL方法不同，CREL的开发是为了识别指定的实体和概念。它还利用Coreference分辨率技术来识别个人实体和对对话中的显式实体提及的引用。我们将Crel与最先进的技术进行比较，并表明它的表现优于所有现有基线。
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.
