Improving Relation Extraction with Knowledge-attention

While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven. We propose a novel knowledge-attention encoder which incorporates prior knowledge from external lexical resources into deep neural networks for relation extraction task. Furthermore, we present three effective ways of integrating knowledge-attention with self-attention to maximize the utilization of both knowledge and data. The proposed relation extraction system is end-to-end and fully attention-based. Experiment results show that the proposed knowledge-attention mechanism has complementary strengths with self-attention, and our integrated models outperform existing CNN, RNN, and self-attention based models. State-of-the-art performance is achieved on TACRED, a complex and large-scale relation extraction dataset.Comment: Paper presented at 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019

Urban-Rural Differences in the Associations of Risk Factors With Epilepsy Based on the California Health Interview Survey: A Multiple Logistic Regression Analysis

Background: Previous studies provided inconsistent associations of smoking, stroke, and serious psychological distress (SPD) with epilepsy while urban-rural differences in the associations of risk factors with epilepsy are not well documented. Objectives: This study aimed to evaluate the associations of lifestyle, health conditions, and SPD with epilepsy and to examine whether the associations differ between urban and rural areas. Patients and Methods: A total of 604 adults with epilepsy and 42416 controls were selected from the 2005 California Health Interview Survey. Weighted univariate and multiple logistic regression analyses were used to estimate the associations of potential factors (behavioral factors, SPD, social factors and health conditions) with epilepsy. The odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. Results: The overall prevalence of epilepsy was 1.3% and the prevalence was higher in urban area than rural area (1.4 vs. 1.1%). The prevalence of SPD was 11% in cases and 4% in controls, respectively. The percentage of stroke was higher in cases than in controls (9% vs. 2%). After adjusting for other factors using multiple logistic regression, current smoking, stroke, cancer, SPD and living in urban were positively significantly associated with epilepsy (OR = 1.74, 95% CI = 1.28 - 2.38; OR = 4.81, 95% CI = 3.13 - 7.41; OR = 1.52, 95% CI = 1.12 - 2.06; OR = 2.02, 95% CI = 1.39 - 2.92, and OR = 1.4, 95% CI = 1.08 - 1.81, respectively); while binge drinking was negatively associated with epilepsy (OR = 0.65, 95% CI = 0.43 - 0.99). Stratified by residence, in the urban area, current smoking and race were only associated with epilepsy. Stroke and SPD showed stronger association with epilepsy in the rural area (OR = 7.63, 95% CI = 3.68 - 15.8, and OR = 3.14, 95% CI = 1.52 - 6.47, respectively) comparing with urban region (OR = 4.51, 95% CI = 2.79 - 7.28 and OR = 1.9, 95% CI = 1.27 - 2.86, respectively)