Recent success of knowledge graphs has spurred interest in applying them in open science,such as on intelligent survey systems for scientists.However,efforts to understand the quality of candidate survey questions pro...Recent success of knowledge graphs has spurred interest in applying them in open science,such as on intelligent survey systems for scientists.However,efforts to understand the quality of candidate survey questions provided by these methods have been limited.Indeed,existing methods do not consider the type of on-the-fly content planning that is possible for face-to-face surveys and hence do not guarantee that selection of subsequent questions is based on response to previous questions in a survey.To address this limitation,we propose a dynamic and informative solution for an intelligent survey system that is based on knowledge graphs.To illustrate our proposal,we look into social science surveys,focusing on ordering the questions of a questionnaire component by their level of acceptance,along with conditional triggers that further customise participants’experience.Our main findings are:(i)evaluation of the proposed approach shows that the dynamic component can be beneficial in terms of lowering the number of questions asked per variable,thus allowing more informative data to be collected in a survey of equivalent length;and(ii)a primary advantage of the proposed approach is that it enables grouping of participants according to their responses,so that participants are not only served appropriate follow-up questions,but their responses to these questions may be analysed in the context of some initial categorisation.We believe that the proposed approach can easily be applied to other social science surveys based on grouping definitions in their contexts.The knowledge-graph-based intelligent survey approach proposed in our work allows online questionnaires to approach face-to-face interaction in their level of informativity and responsiveness,as well as duplicating certain advantages of interview-based data collection.展开更多
There are large and growing textual corpora in which people express contrastive opinions about the same topic.This has led to an increasing number of studies about contrastive opinion mining.However,there are several ...There are large and growing textual corpora in which people express contrastive opinions about the same topic.This has led to an increasing number of studies about contrastive opinion mining.However,there are several notable issues with the existing studies.They mostly focus on mining contrastive opinions from multiple data collections,which need to be separated into their respective collections beforehand.In addition,existing models are opaque in terms of the relationship between topics that are extracted and the sentences in the corpus which express the topics;this opacity does not help us understand the opinions expressed in the corpus.Finally,contrastive opinion is mostly analysed qualitatively rather than quantitatively.This paper addresses these matters and proposes a novel unified latent variable model(contraLDA),which:mines contrastive opinions from both single and multiple data collections,extracts the sentences that project the contrastive opinion,and measures the strength of opinion contrastiveness towards the extracted topics.Experimental results show the effectiveness of our model in mining contrasted opinions,which outperformed our baselines in extracting coherent and informative sentiment-bearing topics.We further show the accuracy of our model in classifying topics and sentiments of textual data,and we compared our results to five strong baselines.展开更多
文摘Recent success of knowledge graphs has spurred interest in applying them in open science,such as on intelligent survey systems for scientists.However,efforts to understand the quality of candidate survey questions provided by these methods have been limited.Indeed,existing methods do not consider the type of on-the-fly content planning that is possible for face-to-face surveys and hence do not guarantee that selection of subsequent questions is based on response to previous questions in a survey.To address this limitation,we propose a dynamic and informative solution for an intelligent survey system that is based on knowledge graphs.To illustrate our proposal,we look into social science surveys,focusing on ordering the questions of a questionnaire component by their level of acceptance,along with conditional triggers that further customise participants’experience.Our main findings are:(i)evaluation of the proposed approach shows that the dynamic component can be beneficial in terms of lowering the number of questions asked per variable,thus allowing more informative data to be collected in a survey of equivalent length;and(ii)a primary advantage of the proposed approach is that it enables grouping of participants according to their responses,so that participants are not only served appropriate follow-up questions,but their responses to these questions may be analysed in the context of some initial categorisation.We believe that the proposed approach can easily be applied to other social science surveys based on grouping definitions in their contexts.The knowledge-graph-based intelligent survey approach proposed in our work allows online questionnaires to approach face-to-face interaction in their level of informativity and responsiveness,as well as duplicating certain advantages of interview-based data collection.
基金the award made by the UK Engineering and Physical Sciences Research Council(EP/P005810/1).
文摘There are large and growing textual corpora in which people express contrastive opinions about the same topic.This has led to an increasing number of studies about contrastive opinion mining.However,there are several notable issues with the existing studies.They mostly focus on mining contrastive opinions from multiple data collections,which need to be separated into their respective collections beforehand.In addition,existing models are opaque in terms of the relationship between topics that are extracted and the sentences in the corpus which express the topics;this opacity does not help us understand the opinions expressed in the corpus.Finally,contrastive opinion is mostly analysed qualitatively rather than quantitatively.This paper addresses these matters and proposes a novel unified latent variable model(contraLDA),which:mines contrastive opinions from both single and multiple data collections,extracts the sentences that project the contrastive opinion,and measures the strength of opinion contrastiveness towards the extracted topics.Experimental results show the effectiveness of our model in mining contrasted opinions,which outperformed our baselines in extracting coherent and informative sentiment-bearing topics.We further show the accuracy of our model in classifying topics and sentiments of textual data,and we compared our results to five strong baselines.
