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Referring expression generation is the part of natural language generation that decides how to refer to the entities appearing in an automatically generated text. Lexicalization is the part of this process which involves the choice of appropriate vocabulary or expressions to transform the conceptual content of a referring expression into the corresponding text in natural language. This problem presents an important challenge when we have enough knowledge to allow more than one alternative. In those cases, we need some heuristics to decide which alternatives are more appropriate in a given situation. Whereas most work on natural language generation has focused on a generic way of generating language, in this paper we explore personal preferences as a type of heuristic that has not been properly addressed. We empirically analyze the TUNA corpus, a corpus of referring expression lexicalizations, to investigate the influence of language preferences in how people lexicalize new referring expressions in different situations. We then present two corpus-based approaches to solve the problem of referring expression lexicalization, one that takes preferences into account and one that does not. The results show a decrease of 50% in the similarity error against the reference corpus when personal preferences are used to generate the final referring expression. 相似文献
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As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information. 相似文献
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Social networking sites (SNSs) enable user to personalize their contents and functions. This feature has been assumed as causing positive effects on the use of online information services through enhancing user satisfaction. However, unlike other online information services (non-participatory information services), due to the results of personalization in a certain situation, SNS users cannot help using the SNS even though they feel dissatisfaction on using it. SNSs are different from other information services in the sense that they create and sustain their own value based on the number of participating members. In SNSs, personalization, reflected by updates and maintenance of profile pages, results in such participation. This study hypothesizes that personalization influences on the continued use of SNSs through two factors: switching cost (extrinsic factor) and satisfaction (intrinsic factor). Web-based survey was conducted with the samples of 677 SNS users from six universities in the US. In-person interviews were conducted with 25 university students to elicit their thoughts on the SNSs. Quantitative analysis employed by testing the proposed model with five hypotheses through a structural equation modeling (SEM) technique. The transcribed interview data was analyzed following the constant comparative technique. The main findings indicate that, as expected, the personalization increases its switching cost as well as satisfaction, which results in further use of SNSs. These findings suggest that it is necessary to consider both extrinsic and intrinsic factors of user perceptions when adding personalization features on SNSs. 相似文献
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采用数据挖掘技术中的关联分析和聚类方法,重点研究Web日志兴趣发现的理论和方法,指出普通日志记录方法的局限性,提出过滤用户偏好的定制Web日志方法,实验结果验证通过该方法采集的数据,可以发现隐藏在日志数据中的关联规则,同时找到相似用户的兴趣和偏好,并且能够提高过滤用户兴趣偏好的精度。 相似文献
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个性化信息服务 ,是针对不同用户采用不同服务策略和方式提供不同信息内容的服务。它具有以用户为中心、对用户需求进行挖掘、灵活多样和主动将信息推送给用户的特点。其类型有 :个性化内容定制服务 ;个性化信息检索定制服务 ;个性化界面定制服务。用户个性化需求可通过用户访问记录挖掘、Bookmark和Agent获取。个性化信息服务模型 ,可采用信息A gent自主学习法、信息过滤法、基于多Multi AgentSystem的合作法等方法构建。参考文献 14。 相似文献
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焦宇锋 《南京体育学院学报(社会科学版)》2001,15(5):22-23
通过对历史的回顾以及对现实人才培养要求的分析．提出了以人为本．促进全面健康发展、面向全体学生，促进共同进步、积极创设教育的环境和方法，促进全面发展和坚持过程评价，结合结果评价的四大具体操作办法。 相似文献
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徐虹 《济宁师范专科学校学报》2004,25(4):37-40
人生的历程是个体社会化的过程，同时也是个体个性化的过程。早在我国的先秦时期，社会化思想已经初步形成。诸子百家的社会化理论、个性化理论和关于个体社会化主要途径的探讨对我们今天的社会生活有着重要的借鉴意义。 相似文献
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Jun Wang Maarten Clements Jie Yang Arjen P. de Vries Marcel J.T. Reinders 《Information processing & management》2010,46(1):58-70
Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata.This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user’s tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user’s own preference and the opinion of others. 相似文献
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