In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks. Recently, as an effective method for tuning Pre-trained Language Models for specific downstream tasks, prompt-learning has attracted a vast amount of attention and research. The main intuition behind the prompt-learning is to insert the template into the input and convert the tasks into equivalent cloze-style tasks. However, most prompt-learning methods only consider the class name and monotonous strategy for knowledge incorporating in cloze-style prediction, which will inevitably incur omissions and bias in short text classification tasks. In this paper, we propose a short text classification method with prompt-learning. Specifically, the top $M$ concepts related to the entity in the short text are retrieved from the open Knowledge Graph like Probase, these concepts are first selected by the distance with class labels, which takes both the short text itself and the class name into consideration during expanding label word space. Then, we conducted four additional strategies for the integration of the expanded concepts, and the union of these concepts are adopted finally in the verbalizer of prompt-learning. Experimental results show that the obvious improvement is obtained compared with other state-of-the-art methods on five well-known datasets.

With the rapid increase of internet information, personalized recommendation systems are an effective way to alleviate the information overload problem, which has attracted extensive attention in recent years. The traditional collaborative filtering utilizes matrix factorization methods to learn hidden feature representations of users and/or items. With deep learning achieved good performance in representation learning, the autoencoder model is widely applied in recommendation systems for the advantages of fast convergence and no label requirement. However, the previous recommendation systems may take the reconstruction output of an autoencoder as the prediction of missing values directly, which may deteriorate their performance and cause unsatisfactory results of recommendation. In addition, the parameters of an autoencoder need to be pre-trained ahead, which greatly increases the time complexity. To address these problems, in this paper, we propose a Hybrid Collaborative Recommendation method via Dual-Autoencoder (HCRDa). More specifically, firstly, a novel dual-autoencoder is utilized to simultaneously learn the feature representations of users and items in our HCRDa, which obviously reduces time complexity. Secondly, embedding matrix factorization into the training process of the autoencoder further improves the quality of hidden features for users and items. Finally, additional attributes of users and items are utilized to alleviate the cold start problem and to make hybrid recommendations. Comprehensive experiments on several real-world data sets demonstrate the effectiveness of our proposed method in comparison with several state-of-the-art methods.