Search for: All records

Network representation learning (NRL) is crucial in the area of graph learning. Recently, graph autoencoders and its variants have gained much attention and popularity among various types of node embedding approaches. Most existing graph autoencoder-based methods aim to minimize the reconstruction errors of the input network while not explicitly considering the semantic relatedness between nodes. In this paper, we propose a novel network embedding method which models the consistency across different views of networks. More specifically, we create a second view from the input network which captures the relation between nodes based on node content and enforce the latent representations from the two views to be consistent by incorporating a multiview adversarial regularization module. The experimental studies on benchmark datasets prove the effectiveness of this method, and demonstrate that our method compares favorably with the state-of-the-art algorithms on challenging tasks such as link prediction and node clustering. We also evaluate our method on a real-world application, i.e., 30-day unplanned ICU readmission prediction, and achieve promising results compared with several baseline methods. 

Question Answering (QA) requires understanding queries expressed in natural languages and relevant information content to provide an answer. For closed-world QAs, information access is by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems. 

Semantic oppositeness is the natural counterpart of the much popular natural language processing concept, semantic similarity. Much like how semantic similarity is a measure of the degree to which two concepts are similar, semantic oppositeness yields the degree to which two concepts would oppose each other. This complementary nature has resulted in most applications and studies incorrectly assuming semantic oppositeness to be the inverse of semantic similarity. In other trivializations, “semantic oppositeness” is used interchangeably with “antonymy”, which is as inaccurate as replacing semantic similarity with simple synonymy. These erroneous assumptions and over-simplifications exist due, mainly, to either lack of information, or the computational complexity of calculation of semantic oppositeness. The objective of this research is to prove that it is possible to extend the idea of word vector embedding to incorporate semantic oppositeness, so that an effective mapping of semantic oppositeness can be obtained in a given vector space. In the experiments we present in this paper, we show that our proposed method achieves a training accuracy of 97.91% and a test accuracy of 97.82%, proving the applicability of this method even in potentially highly sensitive applications and dispelling doubts of over-fitting. Further, this work also introduces a novel, unanchored vector embedding method and a novel, inductive transfer learning process. 

Unplanned intensive care units (ICU) readmissions and in-hospital mortality of patients are two important metrics for evaluating the quality of hospital care. Identifying patients with higher risk of readmission to ICU or of mortality can not only protect those patients from potential dangers, but also reduce the high costs of healthcare. In this work, we propose a new method to incorporate information from the Electronic Health Records (EHRs) of patients and utilize hyperbolic embeddings of a medical ontology (i.e., ICD-9) in the prediction model. The results prove the effectiveness of our method and show that hyperbolic embeddings of ontological concepts give promising performance.