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1.
《Information processing & management》2001,37(3):521-541
We compare a user-defined passage feedback (pf) system to a document feedback (df) system. Df employed the adaptive linear model for retrieval, while pf used weighted query expansion based on positive and negative feedback. Twenty-four searchers performed the same six tasks in varying search and system-order per TREC-8 guidelines. We hypothesized that pf, which featured interactive query expansion, would outperform df, which relied on automatic query expansion. Initial analysis appeared to reject this hypothesis, as df showed slightly higher overall performance than pf. However, analysis by system-order groups indicates only the first pf use had lower performance. These data suggest that pf was more difficult to learn than df, though the second pf use yielded competitive performance. If performance of pf is indeed affected by learning, an improved pf system with usability enhancements may prove to be an effective mechanism for interactive information retrieval. 相似文献
2.
《Information processing & management》2003,39(2):215-231
Recent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different directions of the document space. We investigate ways to improve the effectiveness of the genetic exploration by combining appropriate techniques and heuristics known in genetic theory or in the IR field. Indeed, the approach uses a niching technique to solve the relevance multimodality problem, a relevance feedback technique to perform genetic transformations on query formulations and evolution heuristics in order to improve the convergence conditions of the genetic process. The effectiveness of the global approach is demonstrated by comparing the retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation performed on a subset of TREC-4 using the Mercure IRS. Moreover, experimental results show the positive effect of the various techniques integrated to our genetic algorithm model. 相似文献
3.
Query Expansion (QE) is one of the most important mechanisms in the information retrieval field. A typical short Internet query will go through a process of refinement to improve its retrieval power. Most of the existing QE techniques suffer from retrieval performance degradation due to imprecise choice of query’s additive terms in the QE process. In this paper, we introduce a novel automated QE mechanism. The new expansion process is guided by the semantics relations between the original query and the expanding words, in the context of the utilized corpus. Experimental results of our “controlled” query expansion, using the Arabic TREC-10 data, show a significant enhancement of recall and precision over current existing mechanisms in the field. 相似文献
4.
《Information processing & management》2016,52(2):273-298
In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as “Philip Seymour Hoffman” where the results may require no recency at all. In this work, we focus on this type of queries named “time-sensitive queries” where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community. 相似文献
5.
Francesco Colace Massimo De Santo Luca Greco Paolo Napoletano 《Information processing & management》2015
This paper proposes a novel query expansion method to improve accuracy of text retrieval systems. Our method makes use of a minimal relevance feedback to expand the initial query with a structured representation composed of weighted pairs of words. Such a structure is obtained from the relevance feedback through a method for pairs of words selection based on the Probabilistic Topic Model. We compared our method with other baseline query expansion schemes and methods. Evaluations performed on TREC-8 demonstrated the effectiveness of the proposed method with respect to the baseline. 相似文献
6.
Jamal Abdul Nasir Iraklis Varlamis Samreen Ishfaq 《Information processing & management》2019,56(5):1605-1617
Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets. 相似文献
7.
《Information processing & management》2004,40(1):1-19
This paper presents a new adaptive filtering system called RELIEFS. This system is based on neural mechanisms underlying an information selection process. It is inspired from the cognitive model adaptive resonance theory [Biol. Cybernet. 23 (1976) 121] that proposes a neural explanation of how our brain selects information from its environment. In our approach, resonance, the key idea of this model is used to model the notion of relevance in information retrieval and information filtering (IF). The comparison of resonance with the previous models of relevance shows that resonance captures the very core of most existing models. Moreover, the notion of resonance provides a new angle to look at relevance and opens new theoretical perspectives. The proposed mechanism based on resonance has been directly implemented and tested on the TREC-9 and TREC-11 IF data. The experimental results show that this approach can result in a high effectiveness in practice. 相似文献
8.
In this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike conventional RF algorithms which use the top ranked documents for feedback, our proposed algorithm is a kind of active feedback algorithm which actively chooses documents for the user to judge. The objectives are (a) to increase the number of judged relevant documents and (b) to increase the diversity of judged documents during the RF process. The algorithm uses document-contexts by splitting the retrieval list into sub-lists according to the query term patterns that exist in the top ranked documents. Query term patterns include a single query term, a pair of query terms that occur in a phrase and query terms that occur in proximity. The algorithm is an iterative algorithm which takes one document for feedback in each of the iterations. We experiment with the algorithm using the TREC-6, -7, -8, -2005 and GOV2 data collections and we simulate user feedback using the TREC relevance judgements. From the experimental results, we show that our proposed split-list algorithm is better than the conventional RF algorithm and that our algorithm is more reliable than a similar algorithm using maximal marginal relevance. 相似文献
9.
This paper presents an investigation about how to automatically formulate effective queries using full or partial relevance information (i.e., the terms that are in relevant documents) in the context of relevance feedback (RF). The effects of adding relevance information in the RF environment are studied via controlled experiments. The conditions of these controlled experiments are formalized into a set of assumptions that form the framework of our study. This framework is called idealized relevance feedback (IRF) framework. In our IRF settings, we confirm the previous findings of relevance feedback studies. In addition, our experiments show that better retrieval effectiveness can be obtained when (i) we normalize the term weights by their ranks, (ii) we select weighted terms in the top K retrieved documents, (iii) we include terms in the initial title queries, and (iv) we use the best query sizes for each topic instead of the average best query size where they produce at most five percentage points improvement in the mean average precision (MAP) value. We have also achieved a new level of retrieval effectiveness which is about 55–60% MAP instead of 40+% in the previous findings. This new level of retrieval effectiveness was found to be similar to a level using a TREC ad hoc test collection that is about double the number of documents in the TREC-3 test collection used in previous works. 相似文献
10.
Past research into text retrieval methods for the Web has been restricted by the lack of a test collection capable of supporting experiments which are both realistic and reproducible. The 1.69 million document WT10g collection is proposed as a multi-purpose testbed for experiments with these attributes, in distributed IR, hyperlink algorithms and conventional ad hoc retrieval.WT10g was constructed by selecting from a superset of documents in such a way that desirable corpus properties were preserved or optimised. These properties include: a high degree of inter-server connectivity, integrity of server holdings, inclusion of documents related to a very wide spread of likely queries, and a realistic distribution of server holding sizes. We confirm that WT10g contains exploitable link information using a site (homepage) finding experiment. Our results show that, on this task, Okapi BM25 works better on propagated link anchor text than on full text.WT10g was used in TREC-9 and TREC-2000 and both topic relevance and homepage finding queries and judgments are available. 相似文献
11.
【目的/意义】跨语言信息检索研究的目的即在消除因语言的差异而导致信息查询的困难,提高从大量纷繁
复杂的查找特定信息的效率。同时提供一种更加方便的途径使得用户能够使用自己熟悉的语言检索另外一种语
言文档。【方法/过程】本文通过对国内外跨语言信息检索的研究现状分析,介绍了目前几种查询翻译的方法,包括：
直接查询翻译、文献翻译、中间语言翻译以及查询—文献翻译方法,对其效果进行比较,然后阐述了跨语言检索关
键技术,对使用基于双语词典、语料库、机器翻译技术等产生的歧义性提出了解决方法及评价。【结果/结论】使用自
然语言处理技术、共现技术、相关反馈技术、扩展技术、双向翻译技术以及基于本体信息检索技术确保知识词典的
覆盖度和歧义性处理,通过对跨语言检索实验分析证明采用知识词典、语料库和搜索引擎组合能够提高查询效
率。【创新/局限】本文为了解决跨语言信息检索使用词典、语料库中词语缺乏的现象,提出通过搜索引擎从网页获
取信息资源来充实语料库中语句对不足的问题。文章主要针对中英文信息检索问题进行了探讨,解决方法还需要
进一步研究,如中文切词困难以及字典覆盖率低等严重影响检索的效率。 相似文献
12.
Internet已成为全球最丰富的数据源,数据类型繁杂且动态变化,如何从中快速准确地检索出用户所需要的信息是一个亟待解决的问题.传统的搜索引擎基于语法的方式进行搜索,缺乏语义信息,难以准确地表达用户的查询需求和被检索对象的文档语义,致使查准率和查全率较低且搜索范围有限.本文对现有的语义检索方法进行了研究,分析了其中存在的问题,在此基础上提出了一种基于领域的语义搜索引擎模型,结合语义Web技术,使用领域本体元数据模型对用户的查询进行语义化规范,依据领域本体模式抽取文档中的知识并RDF化,准确地表达了用户的查询语义和作为被查询对象的文档语义,可以大大提高检索的准确性和检索效率,详细地给出了模型的体系结构、基本功能和工作原理. 相似文献
13.
《Information processing & management》2001,37(3):485-505
This paper describes the UC Berkeley's participation in the TREC-6, 7 and 8 interactive track experiments. In these three studies 24 searchers (four in TREC-6, eight in TREC-7, and 12 in TREC-8) conducted a total of 160 searches, half on the Cheshire II system and the other half on the ZPRISE system. In TREC-7 and TREC-8 questionnaires were administered to gather information about basic demographic and searching experience, about each search, about each of the systems, and finally, about the user's perceptions of the systems. In this paper I will briefly describe the systems used in the study and how they differ in design goals and implementation. The results of the interactive track evaluations and the information derived from the questionnaires are then discussed and plans for further research are considered. 相似文献
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15.
《Information processing & management》2019,56(3):1026-1045
The study of query performance prediction (QPP) in information retrieval (IR) aims to predict retrieval effectiveness. The specificity of the underlying information need of a query often determines how effectively can a search engine retrieve relevant documents at top ranks. The presence of ambiguous terms makes a query less specific to the sought information need, which in turn may degrade IR effectiveness. In this paper, we propose a novel word embedding based pre-retrieval feature which measures the ambiguity of each query term by estimating how many ‘senses’ each word is associated with. Assuming each sense roughly corresponds to a Gaussian mixture component, our proposed generative model first estimates a Gaussian mixture model (GMM) from the word vectors that are most similar to the given query terms. We then use the posterior probabilities of generating the query terms themselves from this estimated GMM in order to quantify the ambiguity of the query. Previous studies have shown that post-retrieval QPP approaches often outperform pre-retrieval ones because they use additional information from the top ranked documents. To achieve the best of both worlds, we formalize a linear combination of our proposed GMM based pre-retrieval predictor with NQC, a state-of-the-art post-retrieval QPP. Our experiments on the TREC benchmark news and web collections demonstrate that our proposed hybrid QPP approach (in linear combination with NQC) significantly outperforms a range of other existing pre-retrieval approaches in combination with NQC used as baselines. 相似文献
16.
基于Web资源的信息抽取技术 总被引：7，自引：0，他引：7
Web资源含有大量的有用信息，但由于它们欠结构化，不能为传统的数据库型查询系统所利用。如何将这些信息抽取出来，转化成结构化信息供其它信息集成系统所利用，成为该领域的研究热点。本文介绍了一个简单的Web信息抽取模型，对于基于该模型的wrapper归纳技术进行了探讨，并描述了一个wrapper自动生成系统的原型。 相似文献
17.
18.
《Information processing & management》2001,37(3):435-458
We are interested in how ideas from document clustering can be used to improve the retrieval accuracy of ranked lists in interactive systems. In particular, we are interested in ways to evaluate the effectiveness of such systems to decide how they might best be constructed. In this study, we construct and evaluate systems that present the user with ranked lists and a visualization of inter-document similarities. We first carry out a user study to evaluate the clustering/ranked list combination on instance-oriented retrieval, the task of the TREC-6 Interactive Track. We find that although users generally prefer the combination, they are not able to use it to improve effectiveness. In the second half of this study, we develop and evaluate an approach that more directly combines the ranked list with information from inter-document similarities. Using the TREC collections and relevance judgments, we show that it is possible to realize substantial improvements in effectiveness by doing so, and that although users can use the combined information effectively, the system can provide hints that substantially improve on the user's solo effort. The resulting approach shares much in common with an interactive application of incremental relevance feedback. Throughout this study, we illustrate our work using two prototype systems constructed for these evaluations. The first, AspInQuery, is a classic information retrieval system augmented with a specialized tool for recording information about instances of relevance. The other system, Lighthouse, is a Web-based application that combines a ranked list with a portrayal of inter-document similarity. Lighthouse can work with collections such as TREC, as well as the results of Web search engines. 相似文献
19.
To obtain high performances, previous works on FAQ retrieval used high-level knowledge bases or handcrafted rules. However, it is a time and effort consuming job to construct these knowledge bases and rules whenever application domains are changed. To overcome this problem, we propose a high-performance FAQ retrieval system only using users’ query logs as knowledge sources. During indexing time, the proposed system efficiently clusters users’ query logs using classification techniques based on latent semantic analysis. During retrieval time, the proposed system smoothes FAQs using the query log clusters. In the experiment, the proposed system outperformed the conventional information retrieval systems in FAQ retrieval. Based on various experiments, we found that the proposed system could alleviate critical lexical disagreement problems in short document retrieval. In addition, we believe that the proposed system is more practical and reliable than the previous FAQ retrieval systems because it uses only data-driven methods without high-level knowledge sources. 相似文献