共查询到19条相似文献，搜索用时 15 毫秒
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[目的/意义] 在大数据时代面对海量的数据用户有时会束手无策。因此,越来越多的学者们开始关注互联网热点话题发现的算法,帮助用户快速获取热点话题。[方法/过程] 基于DBSCAN算法,通过动态调整参数来优化算法,实现热点话题发现。根据句法结构与句间关系分析构建热点话题过滤模型,过滤包含热点词项的一般话题。[结果/结论] 采用主流网站新闻数据集进行实验,利用错检率、漏检率等评价指标对算法的有效性进行检验,实验结果证明改进算法性能有所提升,能够为信息用户提供科学研究网络数据的高效途径。 相似文献
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TREC人机交互检索评价项目研究 总被引：1，自引：0，他引：1
介绍TREC交互项目的研究目标、试验设计、评价结果及其发展归宿。将TREC交互项目的发展划分为4个阶段,介绍各阶段在评价指标、试验课题等方面的变化,其中评价指标包括方面查全率、方面查准率、检索耗费时间及用户满意度等。从中可以发现,信息检索评价领域越来越注重“面向用户”的特征。 相似文献
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借鉴生物免疫机制和信息过滤技术,通过生物免疫系统与信息过滤模型映射关系对比,提出了基于人工免疫的不良信息检测器模型,重点对基因库的设计、自体耐受、抗原识别、自适应过滤、协同刺激以及克隆选择等的原理和实现方法进行了阐述。基于以上研究设计了基于人工免疫的网络不良信息过滤系统,并通过仿真实验验证了系统和采用方法的有效性。 相似文献
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问答式信息检索是新一代搜索引擎,它接收自然语言描述的问题,在文档集合中搜索并返回问题的精确答案.问答式信息检索中,检索模块性能的提高将直接影响问题回答系统的整体性能.本文研究系统中的查询优化技术,包括两种策略:基于模式知识库的查询优化;挖掘Web语义蕴含信息,构建查询扩展资源.本文利用TREC提供的问题集与答案集(TREC8-TREC13)做实验来测试查询优化方法的性能,实验结果表明,相对于传统的查询生成,本文采用的查询优化技术在检索精度上取得了提高,t-test结果证明,系统性能提高统计显著. 相似文献
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INEX与TREC是检索领域的两大检索系统评价平台,在检索技术发展迅速的今天依然保持强大生命力,在当今检索技术评价领域起着十分重要的作用。本篇文章通过对INEX与TREC的研究目标以及平台的构成要素包括三个方面:测试集、检索问题的构造、相关性评估的比较,找出INEX相对于TREC评测平台的创新及不同点,以便更加深入和全面地了解INEX的评测方法。 相似文献
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非相关文献知识发现初始集过滤方法的试验研究 总被引：1，自引：0，他引：1
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政府公共危机信息预警能力评价指标体系研究 总被引：2，自引：0，他引：2
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Much work on automated information filtering has been done in the TREC and TDT domains, but differences in corpora, the nature of TREC topics vs. TDT events, the constraints imposed on training and testing, and the choices of performance measures confound any meaningful comparison between these domains. We attempt to bridge the gap between them by evaluating the performance of the k-nearest-neighbor (kNN) classification system on the corpus and categories from one domain using the constraints of the other. To maximize comparability and understand the effect of the evaluation metrics specific to each domain, we optimize the performance of kNN separately for the F
1, T9P (preferred metric for TREC-9) and C
trk (official metric for TDT-3) metrics. Through a thorough comparison of our within-domain and cross-domain results, our results demonstrate that the corpus used for TREC-9 is more challenging for an information filtering system than the TDT-3 corpus and strongly suggest that the TDT-3 event tracking task itself is more difficult than the TREC batch filtering task. We also show that optimizing performance in TREC-9 and TDT-3 tends to result in systems with different performance characteristics, confounding any meaningful comparison between the two domains, and that T9P and C
trk both have properties that make them undesirable as general information filtering metrics. 相似文献
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Stephen Robertson 《Information Retrieval》2002,5(2-3):239-256
An experimental adaptive filtering system, built on the Okapi search engine, is described. In addition to the regular text retrieval functions, the system requires a complex set of procedures for setting score thresholds and adapting them following feedback. These procedures need to be closely related to the evaluation measures to be used. A mixture of quantitative methods relating a threshold to the number of documents expected to be retrieved in a time period, and qualitative methods relating to the probability of relevance, is defined. Experiments under the TREC-9 Adaptive Filtering Track rules are reported. The system is seen to perform reasonably well in comparison with other systems at TREC. Some of the variables that may affect performance are investigated. 相似文献
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Stephen Robertson 《Information Retrieval》2002,5(2-3):127-137
This paper introduces the special issue, and reviews the routing and filtering tasks as defined and evaluated at TREC. The tasks attempt to simulate a specific service situation: the system is assumed to process an incoming stream of documents against profiles of user interest, strictly in the time order in which they arrive, and immediately refer any matching document to the user. In the adaptive filtering version of the task, the user is assumed to provide a relevance judgement instantly. The rationale for the task definitions and the evaluation measures used is discussed. 相似文献
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Anthony Bigot Claude Chrisment Taoufiq Dkaki Gilles Hubert Josiane Mothe 《Information Retrieval》2011,14(6):617-648
To evaluate Information Retrieval Systems on their effectiveness, evaluation programs such as TREC offer a rigorous methodology
as well as benchmark collections. Whatever the evaluation collection used, effectiveness is generally considered globally,
averaging the results over a set of information needs. As a result, the variability of system performance is hidden as the
similarities and differences from one system to another are averaged. Moreover, the topics on which a given system succeeds
or fails are left unknown. In this paper we propose an approach based on data analysis methods (correspondence analysis and
clustering) to discover correlations between systems and to find trends in topic/system correlations. We show that it is possible
to cluster topics and systems according to system performance on these topics, some system clusters being better on some topics.
Finally, we propose a new method to consider complementary systems as based on their performances which can be applied for
example in the case of repeated queries. We consider the system profile based on the similarity of the set of TREC topics
on which systems achieve similar levels of performance. We show that this method is effective when using the TREC ad hoc collection. 相似文献
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依据TREC会议集对历年参与团队与项目进行了统计,重点介绍了中国的TREC历程、TREC-16新推出的Million Query Track,指明了TREC三个未来关注焦点：非正式交流信息、特定学科领域以及用户交互。认为国内研究者应更加关注TREC以及中文语料库的建设。 相似文献
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Stephen Robertson 《Information Retrieval》2002,5(2-3):257-268
Some insight into the behavior of adaptive filtering systems may be gained by comparing them with similar ranked-output retrieval systems. This is not easy; however, a new optimization measure, introduced for the TREC-9 filtering track, makes some such comparison possible. A series of experiments using the TREC-9 filtering data shows that filtering effectiveness is comparable to routing effectiveness, and demonstrates the gains to be made from adaptation. 相似文献
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User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users’ various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is α-nDCG@5 0.307, IA-P@5 0.121, and α#-nDCG@5 0.214 on the TREC09, as well as α-nDCG@10 0.421, IA-P@10 0.201, and α#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users’ search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics. 相似文献
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Coverage-based search result diversification 总被引：1，自引：0，他引：1
Traditional retrieval models may provide users with less satisfactory search experience because documents are scored independently and the top ranked documents often contain excessively redundant information. Intuitively, it is more desirable to diversify search results so that the top-ranked documents can cover different query subtopics, i.e., different pieces of relevant information. In this paper, we study the problem of search result diversification in an optimization framework whose objective is to maximize a coverage-based diversity function. We first define the diversity score of a set of search results through measuring the coverage of query subtopics in the result set, and then discuss how to use them to derive diversification methods. The key challenge here is how to define an appropriate coverage function given a query and a set of search results. To address this challenge, we propose and systematically study three different strategies to define coverage functions. They are based on summations, loss functions and evaluation measures respectively. Each of these coverage functions leads to a result diversification method. We show that the proposed coverage based diversification methods not only cover several state-of-the-art methods but also allows us to derive new ones. We compare these methods both analytically and empirically. Experiment results on two standard TREC collections show that all the methods are effective for diversification and the new methods can outperform existing ones. 相似文献