Learning to Classify Events from Human Needs Category Descriptions (2020)

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.

## introduction training accurate text classifiers often requires a large amount of manually labeled data, which is expensive to collect. in contrast, humans can often perform well on a classification task by only reading category descriptions, which are easy to obtain. it is desirable if computers can automatically learn classifiers from class descriptions. in this work, we aim to learn an event classifier automatically from unlabeled events by using human needs category descriptions as supervision. human needs categories have been proposed to explain why an event is positive or negative @xcite . for example, event "i had cancer" is negative because it violates health needs, while "i had steak" is usually positive because it matches physiological needs. human needs categorization of events @xcite is a task to classify events into eight categories associated with human needs @xcite in psychology: physiological, health, leisure, social, finance, cognition, emotion, and none. ## related work there is a growing interest in studying affective events. some of the previous work @xcite @xcite @xcite aim to recognize the affective polarity of events. recently, there have been many research work focusing on studying human needs and motives @xcite @xcite @xcite to achieve a deeper understanding of sentiment and emotion. however, all these work focused on building classifiers using manually labeled data, or using manual mapping rules @xcite from existing lexicons such as liwc @xcite , which requires a significant amount of manual effort. our work is related to zero-shot learning for text classification @xcite . as @xcite pointed out there are two different settings of zeroshot learning: (1) label-partially-unseen: in which part of the labels are still available for training, and many methods @xcite @xcite have been proposed under this setting; (2) label-fully-unseen: in which all labels are unseen, and it is also called dataless classification in previous work @xcite . our work of learning a classifier from human needs category descriptions is similar to the second setting of label-fully-unseen. researchers have also proposed methods @xcite to learn classifiers from natural language explanations. these methods require both crowdsourced labels and corresponding explanations of the labels, which are not directly applicable to our problem. one key difference between their work and ours is that their methods convert explanations to logical forms or labeling rules as supervision literally, while our work aims to learn classifiers from conceptual descriptions by considering the hyponyms of abstract concepts. for example, in our work we need to understand that the concept "food" in fig.1 means all instances of food, not just the word "food". our work is also related to reinforcement learning which has been used in many nlp applications such as relation classification @xcite , and sentiment analysis @xcite . ## learning classifiers from descriptions our goal is to design an automatic method to learn a classifier from human needs descriptions and unlabeled events. the key idea is to generate weak labels by instantiating abstract concepts with prototypical instances. fig. 2 shows the basic flow of our method. first, we generate candidate concepts from category descriptions and collect prototypical instances for each concept. then, we automatically assign human needs labels to unlabeled events that contain prototypical instances of concepts corresponding to the labels. finally, we train a classifier using the weakly labeled events. to filter noisy concepts, we also design a reinforced selection method to choose high-quality concepts for instantiation. ## conclusion in this work, we proposed a zero-shot learning method to learn a classifier from human needs category descriptions by instantiating abstract concepts with prototypical instances. we also proposed a reinforced concept selection method to select high-quality concepts for instantiation automatically. our experimental results demonstrate that our method achieved significantly better performance than baselines. in our work, we also noticed that the semantics of some events are composed of several concepts. therefore, in the future, it would be worthwhile to explore the compositional concepts from category descriptions further to improve the performance of human needs categorization of events.