UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning

Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train task-specific models for each task. However, application scenarios of ABSA tasks are often diverse. This solution usually requires a large amount of labeled data from each task to perform excellently. These dedicated models are separately trained and separately predicted, ignoring the relationship between tasks. To tackle these issues, we present UnifiedABSA, a general-purpose ABSA framework based on multi-task instruction tuning, which can uniformly model various tasks and capture the inter-task dependency with multi-task learning. Extensive experiments on two benchmark datasets show that UnifiedABSA can significantly outperform dedicated models on 11 ABSA tasks and show its superiority in terms of data efficiency

The dominance of big teams in china’s scientific output

Modern science is dominated by scientific productions from teams. A recent finding shows that teams of both large and small sizes are essential in research, prompting us to analyze the extent to which a country’s scientific work is carried out by big or small teams. Here, using over 26 million publications from Web of Science, we find that China’s research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are written by big teams, China’s drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative work does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small teams than the National Science Foundation (NSF) of the United States does, implying that big teams are preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which indicates a need to balance small and big teams. © 2020 Linlin Liu, Jianfei Yu, Junming Huang, Feng Xia, and Tao Jia. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license

The dominance of big teams in China's scientific output

Modern science is dominated by scientific productions from teams. A recent finding shows that teams with both large and small sizes are essential in research, prompting us to analyze the extent to which a country's scientific work is carried out by big/small teams. Here, using over 26 million publications from Web of Science, we find that China's research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are done by big teams, China's drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative works does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small team works than the National Science Foundation of U.S. (NSF) does, implying that big teams are more preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which urges a need to balance small and big teams

Association between Related Purine Metabolites and Diabetic Retinopathy in Type 2 Diabetic Patients

Aims. The purpose of the study was to investigate the differences of adenosine, adenine, inosine, xanthine, hypoxanthine, and uric acid concentrations in patients with type 2 diabetes mellitus and diabetic retinopathy and assess the relationship between purine metabolites and disease. Materials and Methods. The study group consisted of 114 subjects which were divided into three groups: control (n=40), type 2 diabetes without retinopathy (n=35), and type 2 diabetes with retinopathy (n=39). Levels of metabolites were measured in plasma of all participants. Results. There is a significant increase of levels of adenosine (0.94±0.17 mg/L versus 0.17±0.01 mg/L, P<0.001), inosine (0.297±0.078 mg/L versus 0.086±0.010 mg/L, P<0.001), xanthine (1.01±0.21 mg/L versus 0.54±0.05 mg/L, P=0.009), and uric acid (70.55±3.97 mg/L versus 53.81±2.36 mg/L, P<0.001) with diabetic retinopathy compared to diabetes mellitus. The levels of adenine, hypoxanthine, and xanthine oxidase did not change. Uric acid, xanthine, inosine, and adenosine correlated positively with systolic blood pressure and urea nitrogen. Conclusions. The levels of adenosine, inosine, uric acid, and xanthine may be useful for monitoring the progression of diabetic retinopathy and evaluating the treatment

MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks. However, the datasets currently used in the research are limited to individual elements of specific tasks, usually focusing on in-domain settings, ignoring implicit aspects and opinions, and with a small data scale. To address these issues, we propose a large-scale Multi-Element Multi-Domain dataset (MEMD) that covers the four elements across five domains, including nearly 20,000 review sentences and 30,000 quadruples annotated with explicit and implicit aspects and opinions for ABSA research. Meanwhile, we evaluate generative and non-generative baselines on multiple ABSA subtasks under the open domain setting, and the results show that open domain ABSA as well as mining implicit aspects and opinions remain ongoing challenges to be addressed. The datasets are publicly released at \url{https://github.com/NUSTM/MEMD-ABSA}