Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition
Motion recognition is one of the basic cognitive capabilities of many life forms, however, detecting and understanding motion in text is not a trivial task. In addition, identifying motion entities in natural language is not only challenging but also beneficial for a better natural language understanding. In this paper, we present a Motion Entity Tagging model to identify entities in motion in a text, along with the Literal-Motion-in-Text (LiMiT) dataset used for training the model. We also present results showing that motion features, in particular, entity in motion benefits the Named-Entity Recognition (NER) task. Finally, we present an analysis for the special co-occurrence relation between the person category in NER and animate entities in motion, which significantly improves the classification performance for the person category in NER.