Learning to Classify Events from Human Needs Category Descriptions

Abstract

We study the problem of learning an event classifier from human needs category descriptions, which is challenging due to: (1) the use of highly abstract concepts in natural language descriptions, (2) the difficulty of choosing key concepts. To tackle these two challenges, we propose LEAPI, a zero-shot learning method that first automatically generate weak labels by instantiating high-level concepts with prototypical instances and then trains a human needs classifier with the weakly labeled data. To filter noisy concepts, we design a reinforced selection algorithm to choose high-quality concepts for instantiation. Experimental results on the human needs categorization task show that our method outperforms baseline methods, producing substantially better precision. Physiological NeedsDescription: the need for a person to obtain food, to have meals … foodàfruit, vegetable, meat, egg, fish,… "I bought fruits", "I had eggs this morning" ConceptàInstances Labeled EventsLeisure Needs Description: the need for a person to have leisure activities, to enjoy art … leisure activitiesàfishing, shopping, golf "I went to fishing", "Dad went to play golf" ConceptàInstances Labeled Events