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With the rapid development of information technology, the amount of textual data generated in biomedical field becomes larger and larger. Biomedical event extraction, which is a fundamental information extraction task, has gained a growing interest in biomedical community. Although researchers have proposed various approaches to this task, the performance is still undesirable since previous approaches fail to model biomedical documents effectively. In this paper, we propose an end-to-end framework for document-level joint biomedical event extraction. To better capture the complex relationships among contexts in biomedical documents, a two-level modeling approach is introduced for biomedical documents. More specifically, the dependency-based GCN and hypergraph are used to model local context and global context in each biomedical document, respectively. In addition, a fine-grained interaction mechanism is proposed to model effectively the interaction between local and global contexts to learn better contextualized representations for biomedical event extraction. Comprehensive experiments on two widely used datasets are conducted and the results demonstrate the effectiveness of the proposed framework over state-of-the-art methods.

With the rapid development of information technology, information is generated at an unprecedented rate. Users are in great need of recommender systems to provide the potential friends or interested items for them. Social (i.e. friend) recommendation and behavior (i.e. item) recommendation are two types of popular services in real-world applications. Although researchers have proposed various models for each task, a unified model to address both tasks elegantly and effectively is still in demand. In this paper, we propose a model called SBRNE which integrates social and behavior recommendations into a unified framework through modeling social and behavior information simultaneously. Specifically, SBRNE models social and behavior information simultaneously via employing users’ latent interests as a bridge, and derives improved performance on both social and behavior recommendation tasks. In addition, by introducing an efficient network embedding procedure, users’ latent representations are advanced, and effectiveness and efficiency of recommendation tasks are improved accordingly. Results on both real-world and synthetic datasets demonstrate that: 1). SBRNE outperforms selected baselines on social and behavior recommendation tasks; 2). SBRNE performs stable on recommendation tasks for cold-start users; 3). The network embedding procedure can improve the effectiveness of SBRNE; 4). The hyper-parameter learning procedure can improve both the effectiveness and efficiency of SBRNE.

With the development of social networking applications, microblog has turned to be an indispensable online communication network in our daily life. For microblog users, recommending high quality information is a demanding service. Some microblog services encourage users to annotate themselves with tags, which are used to describe their interests or attributes. However, few users are willing to create tags and available tags are not fully exploited for microblog recommendation. Besides, following/follower relationship in microblog is asymmetric, which can be used not only for communicating with friends or acquaintances but also for getting information on particular subjects. So far, there is no microblog recommendation algorithm which employs all the above mentioned information. This paper aims to investigate a joint framework to combine tag correlation and user social relation for microblog recommendation. Our approach identifies users interests via their personal tags and social relations. More specifically, a user tag retrieval strategy is established to add tags for users without or with few tags, and the user-tag matrix is then built and user-tag weights are then obtained. In order to solve the problem of sparsity of the matrix, both inner and outer correlation between tags are investigated to update the user-tag matrix. Considering the significance of user social relation for microblog recommendation, a useruser social relation similarity matrix is constructed. Moreover, an iterative updating scheme is developed to get the final tag-user matrix for computing the similarities between microblogs and users. We illustrate the capability of our algorithm by making experiments on real microblog datasets. Experimental results show that the algorithm is effective for microblog recommendation.