Social Relation Extraction from Chatbot Conversations: A Shortest Dependency Path Approach

Abstract

Digital dialog systems, also known as chatbots, often lack in the sense of a human-like and individualized interaction. The ability to learn someoneŠs social relations during conversations can lead to more personal responses and therefore to a more human-like and diverse conversation. In this work we present S-REX, a comparison method for extracting social relations from chatbot conversations. The implemented approach uses information from the shortest dependency path in combination with state-of-the-art natural language processing models for entity recognition and semantic word vectors. The method is evaluated on two conversational datasets and achieves results close to more complex neural network methods without the need of extensive training.