共查询到18条相似文献,搜索用时 437 毫秒
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《计算机学报》2014,(8)
现有排序学习算法忽视了查询之间的差异,在建立排序模型的过程中等同对待训练样本集中的所有查询及其相关文档,影响了排序模型的性能.文中描述了查询之间的差异,并在训练过程中考虑查询之间的差异,提出了一种基于有监督学习的多排序模型融合方法.这种方法首先使用每一个查询及其相关文档训练出子排序模型,并将每一个子排序模型的输出转化为体现查询差异的特征数据,使用监督学习方法,实现了多排序模型的融合.更进一步,针对排序问题的特性,文中提出了一种直接优化排序性能的融合函数融合子排序模型,使用梯度上升方法优化其下界函数.文中证明了直接优化排序性能的融合函数融合子排序模型的性能优于子排序模型线性合并的性能.基于较大规模真实数据应用的实验结果表明,直接优化性能指标的多排序模型融合方法可以比传统排序学习模型具有更好的排序性能. 相似文献
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当前排序学习算法在学习时将样本集中的所有查询及其相关文档等同对待,忽略了查询之间以及其相关文档之间的差异性,影响了排序模型的性能。对查询之间的差异进行分析,同时考虑文档排序位置造成的资料被检视概率不同的差异特性,提出了一种两层加权融合的排序方法。该方法为每一个查询及其相关文档建立一个子排序模型,在此过程中,对文档赋予非对称权重,然后通过建立新的损失函数作为优化目标,利用损失函数调节不同查询产生损失之间的权重,最终实现多查询相关排序模型的加权融合。在标准数据集LETOR OHSUMED上的实验结果表明,所提方法在排序性能上有较大提升。 相似文献
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何海江 《模式识别与人工智能》2009,22(6)
代价敏感的排序支持向量机将样本的排序问题转换为样本对的分类问题,以适应Web信息检索.然而急剧膨胀的训练样本对使得学习时间过长.为此,文中提出一种支持二次误差的代价敏感的平滑型排序支持向量机(cs-sRSVM),用分段多项式光滑函数近似铰链损失函数,将优化目标转变为无约束问题.再由Newton-YUAN算法求无约束问题的唯一最优解.在排序学习公开数据集LETOR的实验表明,cs-sRSVM与已有的代价敏感排序算法相比,训练时间更短,而检索性能同样出色. 相似文献
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文档检索结果的排序和文本分类技术是解决垂直搜索、个性化信息检索、信息过滤等相关问题的核心技术。为了提高检索系统的性能,针对Lucene的基础排序算法,提出了一种融合位置相关和概率排序的改进方法。考虑到查询词在文档中出现的位置信息和概率排序对文档相关性的影响,利用位置相关的查询词权值和基于朴素贝叶斯分类算法的文档相关性概率值,对Lucene基础排序算法的评分公式进行改进。实验表明,该改进方法能够有效提高垂直搜索的准确率,使用户拥有更好的垂直搜索体验。 相似文献
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查询扩展作为一门重要的信息检索技术,是以用户查询为基础,通过一定策略在原始查询中加入一些相关的扩展词,从而使得查询能够更加准确地描述用户信息需求。排序学习方法利用机器学习的知识构造排序模型对数据进行排序,是当前机器学习与信息检索交叉领域的研究热点。该文尝试利用伪相关反馈技术,在查询扩展中引入排序学习算法,从文档集合中提取与扩展词相关的特征,训练针对于扩展词的排序模型,并利用排序模型对新查询的扩展词集合进行重新排序,将排序后的扩展词根据排序得分赋予相应的权重,加入到原始查询中进行二次检索,从而提高信息检索的准确率。在TREC数据集合上的实验结果表明,引入排序学习算法有助于提高伪相关反馈的检索性能。 相似文献
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利用机器学习方法自动构建排序模型,在Pairwise方法上平等化每个查询,扩充训练集加大文档不同相关性等级间的区分度和减少不相关文档的噪声影响,利用交叉熵计算误差函数来提高排序算法的性能.在公开数据集LETOR 4.0上的实验结果显示该方法可以提高排序结果的准确率,证明本方法的有效性. 相似文献
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当前,信息检索系统通常采用“检索+重排序”的多级流水线架构。基于稠密表示的检索模型已经被逐渐应用到第一阶段检索中,并展现出了相比传统的稀疏向量空间模型更好的性能。考虑到第一阶段检索所需的高效性,大多数情况下这些模型的基本架构都采用双编码器(bi-encoder)结构。对查询和文档进行独立的编码,分别得到一个稠密表示向量,然后基于获得的查询和文档表示使用简单的相似度函数计算查询-文档对的得分。然而,在编码文档的过程中查询是不可知的,而且文档相比查询而言通常包含更多的主题信息,因此这种简单的单表示模型可能会造成严重的文档信息丢失。为了解决这个问题,设计了一种新的语义检索方法 MDR(multi-representation dense retrieval),将文档编码成多个稠密向量表示。同时,该方法引入覆盖率(coverage)机制来保证多个向量之间的差异性,从而能够覆盖文档中不同主题的信息。为了评估模型性能,在MS MARCO数据集上进行了段落排序和文档排序任务,实验结果证明了MDR方法的有效性。 相似文献
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代价敏感的列表排序算法 总被引:1,自引:0,他引:1
排序学习是信息检索与机器学习中的研究热点之一.在信息检索中,预测排序列表中顶部排序非常重要.但是,排序学习中一类经典的排序算法——列表排序算法——无法强调预测排序列表中顶部排序.为了解决此问题,将代价敏感学习的思想融入到列表排序算法中,提出代价敏感的列表排序算法框架.该框架是在列表排序算法的损失函数中对文档引入权重,且基于性能评价指标NDCG计算文档的权重.在此基础之上,进一步证明了代价敏感的列表排序算法的损失函数是NDCG损失的上界.为了验证代价敏感的列表排序算法的有效性,在此框架下提出了一种代价敏感的ListMLE排序算法,并对该算法开展序保持与泛化性的理论研究工作,从理论上验证了该算法具有序保持特性.在基准数据集上的实验结果表明,在预测排序列表中顶部排序中,代价敏感的ListMLE比传统排序学习算法能取得更好的性能. 相似文献
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Yang Wang Yalou Huang Xiaodong Pang Min Lu Maoqiang Xie Jie Liu 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2013,17(3):421-429
This paper is concerned with supervised rank aggregation, which aims to improve the ranking performance by combining the outputs from multiple rankers. However, there are two main shortcomings in previous rank aggregation approaches. First, the learned weights for base rankers do not distinguish the differences among queries. This is suboptimal since queries vary significantly in terms of ranking. Besides, most current aggregation functions do not directly optimize the evaluation measures in ranking. In this paper, the differences among queries are taken into consideration, and a supervised rank aggregation function is proposed. This aggregation function is directly optimizing the evaluation measure NDCG, referred to as RankAgg.NDCG, We prove that RankAgg.NDCG can achieve better NDCG performance than the linear combination of the base rankers. Experimental results performed on benchmark datasets show our approach outperforms a number of baseline approaches. 相似文献
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在信息检索领域的排序任务中, 神经网络排序模型已经得到广泛使用. 神经网络排序模型对于数据的质量要求极高, 但是, 信息检索数据集通常含有较多噪音, 不能精确得到与查询不相关的文档. 为了训练一个高性能的神经网络排序模型, 获得高质量的负样本, 则至关重要. 借鉴现有方法doc2query的思想, 本文提出了深度、端到... 相似文献
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Plagiarism source retrieval is the core task of plagiarism detection. It has become the standard for plagiarism detection to use the queries extracted from suspicious documents to retrieve the plagiarism sources. Generating queries from a suspicious document is one of the most important steps in plagiarism source retrieval. Heuristic-based query generation methods are widely used in the current research. Each heuristic-based method has its own advantages, and no one statistically outperforms the others on all suspicious document segments when generating queries for source retrieval. Further improvements on heuristic methods for source retrieval rely mainly on the experience of experts. This leads to difficulties in putting forward new heuristic methods that can overcome the shortcomings of the existing ones. This paper paves the way for a new statistical machine learning approach to select the best queries from the candidates. The statistical machine learning approach to query generation for source retrieval is formulated as a ranking framework. Specifically, it aims to achieve the optimal source retrieval performance for each suspicious document segment. The proposed method exploits learning to rank to generate queries from the candidates. To our knowledge, our work is the first research to apply machine learning methods to resolve the problem of query generation for source retrieval. To solve the essential problem of an absence of training data for learning to rank, the building of training samples for source retrieval is also conducted. We rigorously evaluate various aspects of the proposed method on the publicly available PAN source retrieval corpus. With respect to the established baselines, the experimental results show that applying our proposed query generation method based on machine learning yields statistically significant improvements over baselines in source retrieval effectiveness. 相似文献
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Enhancing Concept-Based Retrieval Based on Minimal Term Sets 总被引:1,自引:0,他引:1
A.H. Alsaffar J.S. Deogun V.V. Raghavan H. Sever 《Journal of Intelligent Information Systems》2000,14(2-3):155-173
There is considerable interest in bridging the terminological gap that exists between the way users prefer to specify their information needs and the way queries are expressed in terms of keywords or text expressions that occur in documents. One of the approaches proposed for bridging this gap is based on technologies for expert systems. The central idea of such an approach was introduced in the context of a system called Rule Based Information Retrieval by Computer (RUBRIC). In RUBRIC, user query topics (or concepts) are captured in a rule base represented by an AND/OR tree. The evaluation of AND/OR tree is essentially based on minimum and maximum weights of query terms for conjunctions and disjunctions, respectively. The time to generate the retrieval output of AND/OR tree for a given query topic is exponential in number of conjunctions in the DNF expression associated with the query topic. In this paper, we propose a new approach for computing the retrieval output. The proposed approach involves preprocessing of the rule base to generate Minimal Term Sets (MTSs) that speed up the retrieval process. The computational complexity of the on-line query evaluation following the preprocessing is polynomial in m. We show that the computation and use of MTSs allows a user to choose query topics that best suit their needs and to use retrieval functions that yield a more refined and controlled retrieval output than is possible with the AND/OR tree when document terms are binary. We incorporate p-Norm model into the process of evaluating MTSs to handle the case where weights of both documents and query terms are non-binary. 相似文献
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近年来微博检索已经成为信息检索领域的研究热点。相关的研究表明,微博检索具有时间敏感性。已有工作根据不同的时间敏感性假设,例如,时间越新文档越相关,或者时间越接近热点时刻文档越相关,得到多种不同的检索模型,都在一定程度上提高了检索效果。但是这些假设主要来自于观察,是一种直观简化的假设,仅能从某个方面反映时间因素影响微博排序的规律。该文验证了微博检索具有复杂的时间敏感特性,直观的简化假设并不能准确地描述这种特性。在此基础上提出了一个利用微博的时间特征和文本特征,通过机器学习的方式来构建一个针对时间敏感的微博检索的排序学习模型(TLTR)。在时间特征上,考察了查询相关的全局时间特征以及查询-文档对的局部时间特征。在TREC Microblog Track 20112012数据集上的实验结果表明,TLTR模型优于现有的其他时间敏感的微博排序方法。 相似文献
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《Advanced Engineering Informatics》2015,29(3):366-379
Since engineering design is heavily informational, engineers want to retrieve existing engineering documents accurately during the product development process. However, engineers have difficulties searching for documents because of low retrieval accuracy. One of the reasons for this is the limitation of existing document ranking approaches, in which relationships between terms in documents are not considered to assess the relevance of the retrieved documents. Therefore, we propose a new ranking approach that provides more correct evaluation of document relevance to a given query. Our approach exploits domain ontology to consider relationships among terms in the relevance scoring process. Based on domain ontology, the semantics of a document are represented by a graph (called Document Semantic Network) and, then, proposed relation-based weighting schemes are used to evaluate the graph to calculate the document relevance score. In our ranking approach, user interests and searching intent are also considered in order to provide personalized services. The experimental results show that the proposed approach outperforms existing ranking approaches. A precisely represented semantics of a document as a graph and multiple relation-based weighting schemes are important factors underlying the notable improvement. 相似文献