Institution
University of Stirling
Education•Stirling, Stirling, United Kingdom•
About: University of Stirling is a education organization based out in Stirling, Stirling, United Kingdom. It is known for research contribution in the topics: Population & Polyunsaturated fatty acid. The organization has 7722 authors who have published 20549 publications receiving 732940 citations. The organization is also known as: Stirling University.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors use a Choice Experiment to quantify peoples' preferences over environmental and employment impacts that may result from the deployment of renewable energy projects in rural areas of Scotland, focussing in particular on any differences between the preferences of urban and rural dwellers, and on heterogeneity within these groups.
241 citations
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TL;DR: Evidence is provided that mixed designs are an effective tool for separating transient, trial-related activity from sustained activity in fMRI experiments and can allow researchers a means to examine brain activity associated with sustained processes.
240 citations
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TL;DR: A knowledge-rich solution to targeted aspect-based sentiment analysis with a specific focus on leveraging commonsense knowledge in the deep neural sequential model is proposed and shown to outperform state-of-the-art methods in two targeted aspect sentiment tasks.
Abstract: Sentiment analysis has emerged as one of the most popular natural language processing (NLP) tasks in recent years. A classic setting of the task mainly involves classifying the overall sentiment polarity of the inputs. However, it is based on the assumption that the sentiment expressed in a sentence is unified and consistent, which does not hold in the reality. As a fine-grained alternative of the task, analyzing the sentiment towards a specific target and aspect has drawn much attention from the community for its more practical assumption that sentiment is dependent on a particular set of aspects and entities. Recently, deep neural models have achieved great successes on sentiment analysis. As a functional simulation of the behavior of human brains and one of the most successful deep neural models for sequential data, long short-term memory (LSTM) networks are excellent in learning implicit knowledge from data. However, it is impossible for LSTM to acquire explicit knowledge such as commonsense facts from the training data for accomplishing their specific tasks. On the other hand, emerging knowledge bases have brought a variety of knowledge resources to our attention, and it has been acknowledged that incorporating the background knowledge is an important add-on for many NLP tasks. In this paper, we propose a knowledge-rich solution to targeted aspect-based sentiment analysis with a specific focus on leveraging commonsense knowledge in the deep neural sequential model. To explicitly model the inference of the dependent sentiment, we augment the LSTM with a stacked attention mechanism consisting of attention models for the target level and sentence level, respectively. In order to explicitly integrate the explicit knowledge with implicit knowledge, we propose an extension of LSTM, termed Sentic LSTM. The extended LSTM cell includes a separate output gate that interpolates the token-level memory and the concept-level input. In addition, we propose an extension of Sentic LSTM by creating a hybrid of the LSTM and a recurrent additive network that simulates sentic patterns. In this paper, we are mainly concerned with a joint task combining the target-dependent aspect detection and targeted aspect-based polarity classification. The performance of proposed methods on this joint task is evaluated on two benchmark datasets. The experiment shows that the combination of proposed attention architecture and knowledge-embedded LSTM could outperform state-of-the-art methods in two targeted aspect sentiment tasks. We present a knowledge-rich solution for the task of targeted aspect-based sentiment analysis. Our model can effectively incorporate the commonsense knowledge into the deep neural network and be trained in an end-to-end manner. We show that the two-step attentive neural architecture as well as the proposed Sentic LSTM and H-Sentic-LSTM can achieve an improved performance on resolving the aspect categories and sentiment polarity for a targeted entity in its context over state-of-the-art systems.
240 citations
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TL;DR: These findings complement those from previous studies that show systematic variation in masculinity preferences during the menstrual cycle and suggest that change in testosterone level may play an important role in cyclic shifts in women's preferences for masculine traits.
240 citations
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TL;DR: In this paper, the authors evaluate the contribution of creativity to entrepreneurship theory and practice in terms of building an holistic and transdisciplinary understanding of its impact, including its link with motivation, actualisation and innovation, and the interrogation of entrepreneurial artists as owner/managers.
Abstract: This paper evaluates the contribution of creativity to entrepreneurship theory and practice in terms of building an holistic and transdisciplinary understanding of its impact. Acknowledgement is made of the subjectivist theory of entrepreneurship which embraces randomness, uncertainty and ambiguity but these factors should then be embedded in wider business and social contexts. The analysis is synthesised into a number of themes, from consideration of its definition, its link with personality and cognitive style, creativity as a process and the use of biography in uncovering data on creative entrepreneurial behaviour. Other relevant areas of discussion include creativity's link with motivation, actualisation and innovation, as well as the interrogation of entrepreneurial artists as owner/managers. These factors are embedded in a critical evaluation of how creativity contributes to successful entrepreneurship practice. Modelling, measuring and testing entrepreneurial creativity are also considered and the paper includes detailed consideration of several models of creativity in entrepreneurship. Recommendations for future theory and practice are also made.
239 citations
Authors
Showing all 7824 results
Name | H-index | Papers | Citations |
---|---|---|---|
Paul M. Thompson | 183 | 2271 | 146736 |
Alan D. Baddeley | 137 | 467 | 89497 |
Wolf Singer | 124 | 580 | 72591 |
John J. McGrath | 120 | 791 | 124804 |
Richard J. Simpson | 113 | 850 | 59378 |
David I. Perrett | 110 | 350 | 45878 |
Simon P. Driver | 109 | 455 | 46299 |
David J. Williams | 107 | 2060 | 62440 |
Linqing Wen | 107 | 412 | 70794 |
John A. Raven | 106 | 555 | 44382 |
David Coward | 103 | 400 | 67118 |
Stuart J. H. Biddle | 102 | 484 | 41251 |
Malcolm T. McCulloch | 100 | 371 | 36914 |
Andrew P. Dobson | 98 | 322 | 44211 |
Lister Staveley-Smith | 95 | 599 | 36924 |