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1.
The quality of feedback documents is crucial to the effectiveness of query expansion (QE) in ad hoc retrieval. Recently, machine learning methods have been adopted to tackle this issue by training classifiers from feedback documents. However, the lack of proper training data has prevented these methods from selecting good feedback documents. In this paper, we propose a new method, called AdapCOT, which applies co-training in an adaptive manner to select feedback documents for boosting QE’s effectiveness. Co-training is an effective technique for classification over limited training data, which is particularly suitable for selecting feedback documents. The proposed AdapCOT method makes use of a small set of training documents, and labels the feedback documents according to their quality through an iterative process. Two exclusive sets of term-based features are selected to train the classifiers. Finally, QE is performed on the labeled positive documents. Our extensive experiments show that the proposed method improves QE’s effectiveness, and outperforms strong baselines on various standard TREC collections. 相似文献
2.
Michael J. Cole Jacek Gwizdka Chang Liu Nicholas J. Belkin Xiangmin Zhang 《Information processing & management》2013
The acquisition of information and the search interaction process is influenced strongly by a person’s use of their knowledge of the domain and the task. In this paper we show that a user’s level of domain knowledge can be inferred from their interactive search behaviors without considering the content of queries or documents. A technique is presented to model a user’s information acquisition process during search using only measurements of eye movement patterns. In a user study (n = 40) of search in the domain of genomics, a representation of the participant’s domain knowledge was constructed using self-ratings of knowledge of genomics-related terms (n = 409). Cognitive effort features associated with reading eye movement patterns were calculated for each reading instance during the search tasks. The results show correlations between the cognitive effort due to reading and an individual’s level of domain knowledge. We construct exploratory regression models that suggest it is possible to build models that can make predictions of the user’s level of knowledge based on real-time measurements of eye movement patterns during a task session. 相似文献
3.
This paper is concerned with the quality of training data in learning to rank for information retrieval. While many data selection techniques have been proposed to improve the quality of training data for classification, the study on the same issue for ranking appears to be insufficient. As pointed out in this paper, it is inappropriate to extend technologies for classification to ranking, and the development of novel technologies is sorely needed. In this paper, we study the development of such technologies. To begin with, we propose the concept of “pairwise preference consistency” (PPC) to describe the quality of a training data collection from the ranking point of view. PPC takes into consideration the ordinal relationship between documents as well as the hierarchical structure on queries and documents, which are both unique properties of ranking. Then we select a subset of the original training documents, by maximizing the PPC of the selected subset. We further propose an efficient solution to the maximization problem. Empirical results on the LETOR benchmark datasets and a web search engine dataset show that with the subset of training data selected by our approach, the performance of the learned ranking model can be significantly improved. 相似文献
4.
《Journal of The Franklin Institute》2023,360(13):9754-9787
Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained DL models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, while also outperforming linear baseline models trained from scratch, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states. 相似文献
5.
实施创新驱动离不开创新人才培养,个体创新能力的提升关乎组织的生存和社会的可持续发展。在此背景下,被广泛应用于管理教育、员工甄选和企业内训的商业模拟能否有效提升个体的创新能力仍不得而知。鉴于此,本文依托建构主义学习理论和创新互动观,通过对多期追踪调研数据进行双重差分模型构建和层级回归分析,探讨了商业模拟与创新能力之间的关系,以及背后的作用机理。实证研究发现：商业模拟能够提升参与者的创新能力;商业模拟正向影响个体创新能力的关键驱动因素是商业模拟互动,商业模拟互动不仅直接影响个体创新能力,且通过参与者的心理模拟和绩效压力正向间接影响个体创新能力;此外,当参与者能力与商业模拟挑战在高水平上实现匹配时,商业模拟互动通过绩效压力对创新能力的积极影响被强化,反之则被削弱。研究结论拓展了商业模拟与创新之间关系的理论,从互动视角提出了个体创新能力提升的商业模拟路径。 相似文献
6.
《Information processing & management》2023,60(3):103279
The wide spread of false information has detrimental effects on society, and false information detection has received wide attention. When new domains appear, the relevant labeled data is scarce, which brings severe challenges to the detection. Previous work mainly leverages additional data or domain adaptation technology to assist detection. The former would lead to a severe data burden; the latter underutilizes the pre-trained language model because there is a gap between the downstream task and the pre-training task, which is also inefficient for model storage because it needs to store a set of parameters for each domain. To this end, we propose a meta-prompt based learning (MAP) framework for low-resource false information detection. We excavate the potential of pre-trained language models by transforming the detection tasks into pre-training tasks by constructing template. To solve the problem of the randomly initialized template hindering excavation performance, we learn optimal initialized parameters by borrowing the benefit of meta learning in fast parameter training. The combination of meta learning and prompt learning for the detection is non-trivial: Constructing meta tasks to get initialized parameters suitable for different domains and setting up the prompt model’s verbalizer for classification in the noisy low-resource scenario are challenging. For the former, we propose a multi-domain meta task construction method to learn domain-invariant meta knowledge. For the latter, we propose a prototype verbalizer to summarize category information and design a noise-resistant prototyping strategy to reduce the influence of noise data. Extensive experiments on real-world data demonstrate the superiority of the MAP in new domains of false information detection. 相似文献
7.
Ranking aggregation is a task of combining multiple ranking lists given by several experts or simple rankers to get a hopefully better ranking. It is applicable in several fields such as meta search and collaborative filtering. Most of the existing work is under an unsupervised framework. In these methods, the performances are usually limited especially in unreliable case since labeled information is not involved in. In this paper, we propose a semi-supervised ranking aggregation method, in which preference constraints of several item pairs are given. In our method, the aggregation function is learned based on the ordering agreement of different rankers. The ranking scores assigned by this ranking function on the labeled data should be consistent with the given pairwise order constraints while the ranking scores on the unlabeled data obey the intrinsic manifold structure of the rank items. The experimental results on toy data and the OHSUMED data are presented to illustrate the validity of our method. 相似文献
8.
Users of Social Networking Sites (SNSs) like Facebook, LinkedIn or Twitter, are facing two problems: (1) it is difficult for them to keep track of their social friendships and friends’ social activities scattered across different SNSs; and (2) they are often overwhelmed by the huge amount of social data (friends’ updates and other activities). To address these two problems, we propose a user-centric system called “SocConnect” (Social Connect) for aggregating social data from different SNSs and allowing users to create personalized social and semantic contexts for their social data. Users can blend and group friends on different SNSs, and rate the friends and their activities as favourite, neutral or disliked. SocConnect then provides personalized recommendation of friends’ activities that may be interesting to each user, using machine learning techniques. A prototype is also implemented to demonstrate these functionalities of SocConnect. Evaluation on real users confirms that users generally like the proposed functionalities of our system, and machine learning can be effectively applied to provide personalized recommendation of friends’ activities and help users deal with cognitive overload. 相似文献
9.
现代企业员工培训的柔性化研究——学习理论在企业员工培训中的应用 总被引：1，自引：0，他引：1
在知识经济时代，企业员工培训与其他人力资源管理活动相比具有更重要的战略意义，尤其对于那些以知识型员工为核心的企业，柔性的员工培训管理更具有实际价值。然而，目前我国企业的培训现状存在着培训目标不长远、培训体系不健全、培训效果不明显等诸多弊病，因此，对企业培训管理进行深入的研究势在必行。本文将认知指导理论、自我效能理论、目标设置理论、需求理论、信息加工理论、社会学习理论和成人学习理论这些与员工培训紧密相关的学习理论运用于现代企业培训中。旨在寻求企业培训的最佳途径并形成一套柔性的培训体系，使员工培训成为企业获得长足竞争力的有效途径。 相似文献
10.
11.
This paper describes an ongoing research project that involves the study of teachers’ information seeking behaviors, needs and practices in relation to a collection of primary source materials available through the University of North Carolina at Chapel Hill (UNC) Library’s digital library Documenting the American South (DocSouth). By gaining an in-depth understanding of the needs and wants of teachers in the context of their work, we hope to build a collection of learning objects and a domain ontology applied to the collection to improve teachers’ access to the cultural heritage materials and to facilitate their actual use in the classroom. 相似文献
12.
The main purpose of this paper is to describe the firm-level learning processes by indigenous firms in China, identify the differences between learning processes in small tigers and large dragons, such as China. This paper first sketches the history of the China’s color TV (CTV) industry in which learning processes take place to put the companies case studies into context. Then, this paper examines closely the actual experience of two major Chinese firms in their practice of technological learning. Finally, this paper finds that the most significant difference between learning processes in Chinese firms and these four other tigers’ firms is that, firms from four other tigers usually rely almost exclusively on export markets, but Chinese firms are mostly local market-focused.A novel contribution of this paper is its analysis of the issue: the two Chinese firms have followed not an export-growth path, but an local market-focused path, which proved to be much less successful in countries, such as India, Latin America, Africa and former Soviet Union. The success of this process in China hinges on the five critical factors: (1) a number of multinational firms are increasing their presence in China; (2) there is vibrant competition among domestic firms; (3) the huge domestic market is a key incentive for local firms to invest in technological learning; (4) Chinese central government takes a phased approach to liberalization of the domestic market; (5) there have a number of risk taking entrepreneurs with strategic version, who make investment decisions on learning. These factors should not be easily available to other developing countries. In this regard, China’s experience has limited application to other developing countries. 相似文献
13.
[目的/意义]在大数据和人工智能的大环境下,社会对情报学人才的需求发生了改变。为此,情报学人才的培养也应做出相应的调整。[方法/过程]以问卷调查的形式展开,以情报所工作人员为调查对象,针对实际工作需求,从学历、学科背景、工作经验、跨学科学习经历、学科知识与基本能力等方面分析对情报工作者能力的具体需求,从情报学教学、专业技能培养、学科融合、实践能力培养等方面探讨我国情报学人才培养问题。[结果/结论]年龄在25~35的具有跨学科背景和学习经历的硕士更适合开展现阶段情报工作;同时情报工作者还应具备强有力的分析和洞察数据的能力。情报学人才培养应围绕数据分析流程,融合数据科学的学科内容,依次从课程设置、具体专业技能培养和实践环节展开。 相似文献
14.
Question answering websites are becoming an ever more popular knowledge sharing platform. On such websites, people may ask any type of question and then wait for someone else to answer the question. However, in this manner, askers may not obtain correct answers from appropriate experts. Recently, various approaches have been proposed to automatically find experts in question answering websites. In this paper, we propose a novel hybrid approach to effectively find experts for the category of the target question in question answering websites. Our approach considers user subject relevance, user reputation and authority of a category in finding experts. A user’s subject relevance denotes the relevance of a user’s domain knowledge to the target question. A user’s reputation is derived from the user’s historical question-answering records, while user authority is derived from link analysis. Moreover, our proposed approach has been extended to develop a question dependent approach that considers the relevance of historical questions to the target question in deriving user domain knowledge, reputation and authority. We used a dataset obtained from Yahoo! Answer Taiwan to evaluate our approach. Our experiment results show that our proposed methods outperform other conventional methods. 相似文献
15.
为了有效提高企业创新绩效,本文以技术知识为切入点,将其解构为领域知识相似性和架构知识差异性,探究对企业创新绩效的差异化影响,同时,揭示了惯例复制的内在传导机制。结合我国高技术行业的调研数据,运用多元层级回归分析进行假设检验。结果表明：领域知识相似性和架构知识差异性对创新绩效均存在正向影响;常规惯例复制和柔性惯例复制具有根源性差异,对创新绩效产生正向影响;领域知识相似性和架构知识差异性对惯例复制的促进作用存在差异,领域知识相似性对柔性惯例复制的正向影响更强,架构知识差异性对常规惯例复制的正向影响更强;常规惯例复制和柔性惯例复制的中介效应具有差异化,其常规惯例复制的中介效应均强于柔性惯例复制。本研究揭示了技术知识、惯例复制对创新绩效影响的内在机理,有利于指明企业创新绩效的提升机制。 相似文献
16.
《Information processing & management》2022,59(2):102857
The impact of crisis events can be devastating in a multitude of ways, many of which are unpredictable due to the suddenness in which they occur. The evolution of social media (for example Twitter) has given directly affected individuals or those with valuable information a platform to effectively share their stories to the masses. As a result, these platforms have become vast repositories of helpful information for emergency organizations. However, different crisis events often contain event-specific keywords, which results in the difficult extraction of useful information with a single model. In this paper, we put forward TASR, which stands for Topic-Agnostic Stylometric Representations, a novice deep learning architecture that uses stylometric and adversarial learning to remove topical bias to better manage the unknown surrounding unseen events. As an alternative to domain adaptive approaches requiring data from the unseen event, it reduces the work for those responding to the onset of a crisis. Overall, we conduct a comprehensive study of the situational properties of TASR, the benefits of its architecture including its topic-agnostic and explainable properties, and how it improves upon comparable models in past research. From two experiments, on average, TASR is able to outperform state-of-the-art methods such as transfer learning and domain adoption by 11% in AUC. The ablation study illustrates how different architecture choices of TASR impact the results and that TASR has been optimized for this task. Finally, we conduct a case study to show that explainable results from our model can be used to help guide human analysts through crisis information extraction. 相似文献
17.
First- and second-order additionality and learning outcomes in collaborative R&D programs 总被引：1，自引：0，他引：1
In this paper, we distinguish between firm-level learning effects that result from ‘first-order’ and ‘second-order’ additionalities in innovation policy interventions. ‘First-order’ additionalities represent direct firm-level R&D subsidies, whereas ‘second-order’ additionalities result from knowledge spill-overs, horizontal knowledge exchanges between firms, and from other meso- or community-level effects. Analyzing data from collaborative R&D programs in Finland, we show that enhancing identification with a community of practice among R&D program participants (proxy for second-order additionality) enhances firm-level learning outcomes beyond those resulting from direct R&D subsidy (proxy for first-order additionality). Learning effects facilitated by second-order additionality are not confined to technological learning alone, encompassing also business and market learning. We also show that aspects of program implementation enhance identification with a community of practice, which then mediate the relationship between program implementation and firm-level learning outcomes. 相似文献
18.
《Information processing & management》2005,41(2):217-242
POSIE (POSTECH Information Extraction System) is an information extraction system which uses multiple learning strategies, i.e., SmL, user-oriented learning, and separate-context learning, in a question answering framework. POSIE replaces laborious annotation with automatic instance extraction by the SmL from structured Web documents, and places the user at the end of the user-oriented learning cycle. Information extraction as question answering simplifies the extraction procedures for a set of slots. We introduce the techniques verified on the question answering framework, such as domain knowledge and instance rules, into an information extraction problem. To incrementally improve extraction performance, a sequence of the user-oriented learning and the separate-context learning produces context rules and generalizes them in both the learning and extraction phases. Experiments on the “continuing education” domain initially show that the F1-measure becomes 0.477 and recall 0.748 with no user training. However, as the size of the training documents grows, the F1-measure reaches beyond 0.75 with recall 0.772. We also obtain F-measure of about 0.9 for five out of seven slots on “job offering” domain. 相似文献
19.
Arthur M Diamond Jr. 《Research Policy》2003,32(9):1607-1617
Edwin Mansfield’s contributions to the economics of technology are summarized from the early 1960s through his death in 1997. Mansfield’s methodology is discussed, as are his contributions on: the diffusion of technical innovation, the effect of firm size on innovation, the role of academic and basic research in increasing innovation and productivity, international technology transfer and the inaccuracy of technological forecasts. The economics profession’s evaluation of the relative importance of Mansfield’s work is presented, using as evidence citation counts of his works collected from the Social Science Citation Index (SSCI). Identified as among Mansfield’s most important contributions are his work on the importance of academic research for industrial innovations, his empirical estimation of the rates of diffusion of different innovations, and his estimation of the private and social returns from investments in industrial innovations. Finally, we present Mansfield’s advice on the future of the economics of technology. 相似文献
20.
《International Journal of Information Management》2017,37(4):284-296
The initial learning experience is crucial for understanding digital services adoption and usage diffusion. Using a UTAUTv2 model, we explore the effect of process- and content-oriented knowledge on behavioral intentions to use e-government services. The adoption of e-government systems is lower than desired in general and faces considerable resistance in many developing countries. Scholars suggest that more knowledge and better training are critical to increasing adoption and usage rates. We conducted a survey of 262 citizens in Lebanon to investigate how consumers cope with high and moderate levels of complexity during their initial learning experience with a technology-based product. The results show that a moderate degree of content- and process-oriented knowledge about e-government services during an initial learning experience improves usage habits, performance expectancy, effort expectancy, and facilitating conditions. The challenge for service providers is to understand consumers’ learning experience and coping strategies and to provide mechanisms that make the transition to e-services easier and more intuitive. This can be achieved by developing new infrastructure for e-services to facilitate easier access to e-government websites and to improve site performance. Marketers can also develop more effective communications that offer easy and flexible specific steps for using the portal. 相似文献