Text classification is a fundamental and central position in natural language processing. There are many solutions to the text classification problem, but few use the semantic combination of multiple perspectives to improve the classification performance. The paper proposes a dual-channel attention network model called DCAT, which uses the complementarity between semantics to refine the understanding deficit. Specifically, DCAT first captures the logical semantics of the text through transductive learning and graph structure. Then, at the attention fusion layer (Channel), we use the logical semantics to perform joint semantic training on other semantics to correct the predictions of unlabeled test data incrementally. Experiments show that DCAT can achieve more accurate classification on a wide range of text classification datasets, which is vital for subsequent text mining tasks.

Knowledge base question answering (KBQA) can be divided into two types according to the type of complexity: questions with constraints and questions with multiple hops of relationships. Previous work on knowledge base question answering have mostly focused on entities and relations. In a multihop question, it is insufficient to focus solely on topic entities and their relations since the relation between words also contains some important information. In addition, because the question contains constraints or multiple relationships, the information is difficult to capture, or the constraints are missed. In this paper, we applied a dependency structure to questions that capture relation information (e.g., constraint) between the words in question through a graph convolution network. The captured relation information is integrated into the question for re-encoding, and the information is used to generate and rank query graphs. Compared with existing sequence models and query graph generation models, our approach achieves a 0.8–3% improvement on two benchmark datasets.

Personalized chatbots concentrate on learning human personalities, making them act similar to real users. When it is authorized to respond to other people’s messages, it has the same way of speaking as the user. Many personalized methods have been proposed to use several persona descriptions or key-value-based persona information to assign a personality for dialogue chatbots. Most of them employ explicit user profiles. However, obtaining generous explicit user profiles are extremely time-consuming and requires tremendous manual labor. In addition, explicit user profiles cannot be updated as the user’s interests change. In this article, we propose a generation-based personalized chatbot model, IMDPchat, that learns latent user representation from the abundant users’ dialogue history. Specially, we train a personalized language model to build a global user profile using dialogue responses. To take full advantage of users’ information used in the historical dialogue, we establish a key-value memory network and construct a post-sensitive personalized selection module. The above two parts is context-aware: we endow higher weights to historical post-response pairs that are connected to the current post. To predict more personalized responses, we design a personalized response decoder that can well integrate two decoding modes, including generating tokens and copying personalized words. Experimental results indicate that the IMDPchat model outperforms previous baselines remarkably.