共查询到17条相似文献，搜索用时 156 毫秒
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针对现有信息检索系统中存在的词不匹配问题,提出一种基于词间关联规则的查询扩展算法,该算法利用现有挖掘算法自动对前列初检文档进行词间关联挖掘,提取含有原查询词的词间关联规则,从中提取扩展词,实现查询扩展。实验结果表明,该算法能改善和提高信息检索系统的查全率和查准率,具有很高的应用价值,与未进行查询扩展时相比,采用本文查询扩展算法后,平均准确率提高了13.34%,与传统的局部上下文分析查询扩展算法比较,其平均准确率提高了4.87%。 相似文献
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基于Apriori改进算法的局部反馈查询扩展 总被引：1，自引：0，他引：1
提出面向查询扩展的Apriori改进算法，采用三种剪枝策略，极大提高挖掘效率；针对现有查询扩展存在的缺陷，提出基于Apriori改进算法的局部反馈查询扩展算法，该算法用Apriori改进算法对前列初检文档进行词间关联规则挖掘，提取含有原查询词的词间关联规则，构造规则库，从库中提取扩展词，实现查询扩展。实验结果表明该算法能够提高信息检索性能，与现有算法比较，在相同查全率水平级下其平均查准率有了明显提高。 相似文献
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面向查询扩展的特征词频繁项集挖掘算法 总被引：1，自引：0，他引：1
为了获取高质量的扩展词,提出一种面向查询扩展的基于文本数据库的特征词频繁项集挖掘算法。该算法采用支持度衡量特征词频繁项集,给出新的剪枝策略,并结合原始查询,挖掘同时含有查询词项和非查询词项的特征词频繁项集,以提高挖掘效率。实验表明,与传统的挖掘算法相比,本算法更有效、更合理。 相似文献
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基于用户相关反馈的带结构语义的XML查询词扩展 总被引：1，自引：0，他引：1
在XML文档的信息检索中,检索质量不高的一个主要原因是用户难以提出准确描述其查询意图的查询表达式,而查询扩展技术被认为是可以帮助用户构建符合其查询意图的查询表达式.本文在XML信息检索中提出了基于用户相关反馈的查询扩展技术,在查询扩展中除了考虑词频因素外还充分考虑了XML文档的结构特点对于扩展查询词选取的影响,包括文档中元素的语义权重、元素所在层次和词项与初始查询词间的距离因素对于扩展查询词选取的影响.实验证明本方法是可行的,且能较好地提高检索结果的准确率. 相似文献
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查询扩展技术通过向初始查询请求加入相似或相关的词,组成更为准确的扩展查询表达式,来减少查询请求与相关文献在表达上的不匹配现象,改善检索性能.与传统的查询扩展不同,XML查询扩展不仅要对文档内容进行有效扩展,而且还要考虑结构扩展.本文提出了一种基于伪反馈的XML查询扩展方法,将初始检索结果聚类,获得与查询请求最为相关的文档簇,然后在文档簇中抽取词组,找到符合用户查询意图的扩展查询词组,并在扩展查询词组的基础上进行结构扩展,最终形成完整的"内容+结构"的查询扩展表达式.相关实验结果表明,相对没有扩展的查询,所提方法具有更好的精度. 相似文献
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针对传统TF-IDF在文本过滤时存在的缺点,提出一种基于特征词抽取的文本过滤算法。简要分析文档信息过滤原理和流程,重点讨论文档信息过滤算法设计及技术实现。实验结果表明,所提出的算法可有效对文档信息进行过滤,能够提高信息检索质量。 相似文献
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Efficient information searching and retrieval methods are needed to navigate the ever increasing volumes of digital information. Traditional lexical information retrieval methods can be inefficient and often return inaccurate results. To overcome problems such as polysemy and synonymy, concept-based retrieval methods have been developed. One such method is Latent Semantic Indexing (LSI), a vector-space model, which uses the singular value decomposition (SVD) of a term-by-document matrix to represent terms and documents in k-dimensional space. As with other vector-space models, LSI is an attempt to exploit the underlying semantic structure of word usage in documents. During the query matching phase of LSI, a user's query is first projected into the term-document space, and then compared to all terms and documents represented in the vector space. Using some similarity measure, the nearest (most relevant) terms and documents are identified and returned to the user. The current LSI query matching method requires that the similarity measure be computed between the query and every term and document in the vector space. In this paper, the kd-tree searching algorithm is used within a recent LSI implementation to reduce the time and computational complexity of query matching. The kd-tree data structure stores the term and document vectors in such a way that only those terms and documents that are most likely to qualify as nearest neighbors to the query will be examined and retrieved. 相似文献
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Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users’ social media data. 相似文献
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In the information retrieval process, functions that rank documents according to their estimated relevance to a query typically regard query terms as being independent. However, it is often the joint presence of query terms that is of interest to the user, which is overlooked when matching independent terms. One feature that can be used to express the relatedness of co-occurring terms is their proximity in text. In past research, models that are trained on the proximity information in a collection have performed better than models that are not estimated on data. We analyzed how co-occurring query terms can be used to estimate the relevance of documents based on their distance in text, which is used to extend a unigram ranking function with a proximity model that accumulates the scores of all occurring term combinations. This proximity model is more practical than existing models, since it does not require any co-occurrence statistics, it obviates the need to tune additional parameters, and has a retrieval speed close to competing models. We show that this approach is more robust than existing models, on both Web and newswire corpora, and on average performs equal or better than existing proximity models across collections. 相似文献
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In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve
this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments.
Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it
is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach
is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study,
we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations
and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form
query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation
shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on
TREC retrieval. 相似文献
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The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval; examining, separately and in combination, the utility of term reweighting and query expansion using a probabilistic retrieval model. Retrieval performance is evaluated in terms of standard precision-recall measures, and also using number-to-view graphs. Experimental results, on the standard BMIR-J2 Japanese language retrieval collection, show that both term reweighting and query expansion improve retrieval performance. This is reflected in improvements in both precision and recall, but also a reduction in the average number of documents which must be viewed to find a selected number of relevant items. In particular, using a simple simulation of user searching, incremental application of relevance information is shown to lead to progressively improved retrieval performance and an overall reduction in the number of documents that a user must view to find relevant ones. 相似文献
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Relevance feedback methods generally suffer from topic drift caused by word ambiguities and synonymous uses of words. Topic drift is an important issue in patent information retrieval as people tend to use different expressions describing similar concepts causing low precision and recall at the same time. Furthermore, failing to retrieve relevant patents to an application during the examination process may cause legal problems caused by granting an existing invention. A possible cause of topic drift is utilizing a relevance feedback-based search method. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with phrases disambiguating the original query words. The idea was implemented for patent search where patents are classified into a hierarchy of categories, and the analyses of the experimental results showed not only the positive roles of phrases and words in retrieving additional relevant documents through query expansion but also their contributions to alleviating the query drift problem. More specifically, our query expansion method was compared against relevance-based language model, a state-of-the-art query expansion method, to show its superiority in terms of MAP on all levels of the classification hierarchy. 相似文献
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检索词自动扩展词库构建方法的基本思路是：根据语料是否规范化处理进行词库分类建设,优化了系统的检索性能;结合学科类别,对词库语料进行领域划分,引导科技人员对技术领域的准确把握;建设以本体库为基础,将与规范词具有关联性、相似性的语料通过关系表与关联库关联,把科技文献中的关键词组成一个有序的关系网,解决了传统检索系统中检索词无关联的不足;通过对检索词出现频率进行统计分析,进而更新词库,保证本体库、关联库语料的时效性,突破了人工对词库更新管理的受限性。 相似文献