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1.
Cluster-based and passage-based document retrieval paradigms were shown to be effective. While the former are based on utilizing
query-related corpus context manifested in clusters of similar documents, the latter address the fact that a document can
be relevant even if only a very small part of it contains query-pertaining information. Hence, cluster-based approaches could
be viewed as based on “expanding” the document representation, while passage-based approaches can be thought of as utilizing
a “contracted” document representation. We present a study of the relative benefits of using each of these two approaches,
and of the potential merits of their integration. To that end, we devise two methods that integrate whole-document-based,
cluster-based and passage-based information. The methods are applied for the re-ranking task, that is, re-ordering documents in an initially retrieved list so as to improve precision at the very top ranks. Extensive
empirical evaluation attests to the potential merits of integrating these information types. Specifically, the resultant performance
substantially transcends that of the initial ranking; and, is often better than that of a state-of-the-art pseudo-feedback-based
query expansion approach. 相似文献
2.
Precision prediction based on ranked list coherence 总被引：1，自引：0，他引：1
We introduce a statistical measure of the coherence of a list of documents called the clarity score. Starting with a document list ranked by the query-likelihood retrieval model, we demonstrate the score's relationship to query ambiguity with respect to the collection. We also show that the clarity score is correlated with the average precision of a query and lay the groundwork for useful predictions by discussing a method of setting decision thresholds automatically. We then show that passage-based clarity scores correlate with average-precision measures of ranked lists of passages, where a passage is judged relevant if it contains correct answer text, which extends the basic method to passage-based systems. Next, we introduce variants of document-based clarity scores to improve the robustness, applicability, and predictive ability of clarity scores. In particular, we introduce the ranked list clarity score that can be computed with only a ranked list of documents, and the weighted clarity score where query terms contribute more than other terms. Finally, we show an approach to predicting queries that perform poorly on query expansion that uses techniques expanding on the ideas presented earlier.
相似文献
| W. Bruce CroftEmail: |
3.
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
4.
A structured document retrieval (SDR) system aims to minimize the effort users spend to locate relevant information by retrieving parts of documents. To evaluate the range of SDR tasks, from element to passage to tree retrieval, numerous task-specific measures have been proposed. This has resulted in SDR evaluation measures that cannot easily be compared with respect to each other and across tasks. In previous work, we defined the SDR task of tree retrieval where passage and element are special cases. In this paper, we look in greater detail into tree retrieval to identify the main components of SDR evaluation: relevance, navigation, and redundancy. Our goal is to evaluate SDR within a single probabilistic framework based on these components. This framework, called Extended Structural Relevance (ESR), calculates user expected gain in relevant information depending on whether it is seen via hits (relevant results retrieved), unseen via misses (relevant results not retrieved), or possibly seen via near-misses (relevant results accessed via navigation). We use these expectations as parameters to formulate evaluation measures for tree retrieval. We then demonstrate how existing task-specific measures, if viewed as tree retrieval, can be formulated, computed and compared using our framework. Finally, we experimentally validate ESR across a range of SDR tasks. 相似文献
5.
This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments (General and Specific) and two categories of topics (Broad and Narrow). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call Coherent Retrieval Elements. The results of our experiments show that—when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics)—the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval. 相似文献
6.
The Web contains a tremendous amount of information. It is challenging to determine which Web documents are relevant to a user query, and even more challenging to rank them according to their degrees of relevance. In this paper, we propose a probabilistic retrieval model using logistic regression for recognizing multiple-record Web documents against an application ontology, a simple conceptual modeling approach. We notice that many Web documents contain a sequence of chunks of textual information, each of which constitutes a record. This type of documents is referred to as multiple-record documents. In our categorization approach, a document is represented by a set of term frequencies of index terms, a density heuristic value, and a grouping heuristic value. We first apply the logistic regression analysis on relevant probabilities using the (i) index terms, (ii) density value, and (iii) grouping value of each training document. Hereafter, the relevant probability of each test document is interpolated from the fitting curves. Contrary to other probabilistic retrieval models, our model makes only a weak independent assumption and is capable of handling any important dependent relationships among index terms. In addition, we use logistic regression, instead of linear regression analysis, because the relevance probabilities of training documents are discrete. Using a test set of car-ads and another one for obituary Web documents, our probabilistic model achieves the averaged recall ratio of 100%, precision ratio of 83.3%, and accuracy ratio of 92.5%. 相似文献
7.
Query structuring and expansion with two-stage term dependence for Japanese web retrieval 总被引：1，自引：1，他引：0
In this paper, we propose a new term dependence model for information retrieval, which is based on a theoretical framework
using Markov random fields. We assume two types of dependencies of terms given in a query: (i) long-range dependencies that
may appear for instance within a passage or a sentence in a target document, and (ii) short-range dependencies that may appear
for instance within a compound word in a target document. Based on this assumption, our two-stage term dependence model captures
both long-range and short-range term dependencies differently, when more than one compound word appear in a query. We also
investigate how query structuring with term dependence can improve the performance of query expansion using a relevance model.
The relevance model is constructed using the retrieval results of the structured query with term dependence to expand the
query. We show that our term dependence model works well, particularly when using query structuring with compound words, through
experiments using a 100-gigabyte test collection of web documents mostly written in Japanese. We also show that the performance
of the relevance model can be significantly improved by using the structured query with our term dependence model.
相似文献
| Koji EguchiEmail: |
8.
Ben Carterette 《Information Retrieval》2011,14(1):89-106
A useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible intents of a query with some distribution, or should contain a diverse set of subtopics
related to the user’s information need, or contain nuggets of information with little redundancy. Evaluation measures have
been introduced to measure the effectiveness of systems at this task, but these measures have worst-case NP-hard computation
time. The primary consequence of this is that there is no ranking principle akin to the Probability Ranking Principle for
document relevance that provides uniform instruction on how to rank documents for novelty and diversity. We use simulation
to investigate the practical implications of this for optimization and evaluation of retrieval systems. 相似文献
9.
In many probabilistic modeling approaches to Information Retrieval we are interested in estimating how well a document model
“fits” the user’s information need (query model). On the other hand in statistics, goodness of fit tests are well established
techniques for assessing the assumptions about the underlying distribution of a data set. Supposing that the query terms are
randomly distributed in the various documents of the collection, we actually want to know whether the occurrences of the query
terms are more frequently distributed by chance in a particular document. This can be quantified by the so-called goodness
of fit tests. In this paper, we present a new document ranking technique based on Chi-square goodness of fit tests. Given
the null hypothesis that there is no association between the query terms q and the document d irrespective of any chance occurrences, we perform a Chi-square goodness of fit test for assessing this hypothesis and calculate
the corresponding Chi-square values. Our retrieval formula is based on ranking the documents in the collection according to
these calculated Chi-square values. The method was evaluated over the entire test collection of TREC data, on disks 4 and
5, using the topics of TREC-7 and TREC-8 (50 topics each) conferences. It performs well, outperforming steadily the classical
OKAPI term frequency weighting formula but below that of KL-Divergence from language modeling approach. Despite this, we believe
that the technique is an important non-parametric way of thinking of retrieval, offering the possibility to try simple alternative
retrieval formulas within goodness-of-fit statistical tests’ framework, modeling the data in various ways estimating or assigning any arbitrary theoretical distribution
in terms. 相似文献
10.
Blog feed search aims to identify a blog feed of recurring interest to users on a given topic. A blog feed, the retrieval
unit for blog feed search, comprises blog posts of diverse topics. This topical diversity of blog feeds often causes performance
deterioration of blog feed search. To alleviate the problem, this paper proposes several approaches based on passage retrieval,
widely regarded as effective to handle topical diversity at document level in ad-hoc retrieval. We define the global and local
evidence for blog feed search, which correspond to the document-level and passage-level evidence for passage retrieval, respectively,
and investigate their influence on blog feed search, in terms of both initial retrieval and pseudo-relevance feedback. For
initial retrieval, we propose a retrieval framework to integrate global evidence with local evidence. For pseudo-relevance
feedback, we gather feedback information from the local evidence of the top K ranked blog feeds to capture diverse and accurate information related to a given topic. Experimental results show that our
approaches using local evidence consistently and significantly outperform traditional ones. 相似文献
11.
As the volume and variety of information sources continues to grow, there is increasing difficulty with respect to obtaining
information that accurately matches user information needs. A number of factors affect information retrieval effectiveness
(the accuracy of matching user information needs against the retrieved information). First, users often do not present search
queries in the form that optimally represents their information need. Second, the measure of a document’s relevance is often
highly subjective between different users. Third, information sources might contain heterogeneous documents, in multiple formats
and the representation of documents is not unified. This paper discusses an approach for improvement of information retrieval
effectiveness from document databases. It is proposed that retrieval effectiveness can be improved by applying computational
intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative
(subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An
information retrieval is developed whose retrieval effectiveness is evaluated using traditional precision and recall. 相似文献
12.
Robert W. P. Luk 《Information Retrieval》2008,11(6):539-561
This paper discusses various issues about the rank equivalence of Lafferty and Zhai between the log-odds ratio and the query
likelihood of probabilistic retrieval models. It highlights that Robertson’s concerns about this equivalence may arise when
multiple probability distributions are assumed to be uniformly distributed, after assuming that the marginal probability logically
follows from Kolmogorov’s probability axioms. It also clarifies that there are two types of rank equivalence relations between
probabilistic models, namely strict and weak rank equivalence. This paper focuses on the strict rank equivalence which requires
the event spaces of the participating probabilistic models to be identical. It is possible that two probabilistic models are
strict rank equivalent when they use different probability estimation methods. This paper shows that the query likelihood,
p(q|d, r), is strict rank equivalent to p(q|d) of the language model of Ponte and Croft by applying assumptions 1 and 2 of Lafferty and Zhai. In addition, some statistical
component language model may be strict rank equivalent to the log-odds ratio, and that some statistical component model using
the log-odds ratio may be strict rank equivalent to the query likelihood. Finally, we suggest adding a random variable for
the user information need to the probabilistic retrieval models for clarification when these models deal with multiple requests. 相似文献
13.
将自然语言处理技术——统计语言模型引入信息检索领域产生了一系列全新的检索模型,典型包括查询似然模型、生成相关性模型、词项依赖模型、统计翻译模型、泊松分布模型以及风险最小化框架等。本文从统计学模型以及N-gram技术的角度重点解析这些信息检索模型的演进过程。最后对基于统计语言模型的信息检索模型的发展过程以及未来发展趋势和挑战进行了总结。 相似文献
14.
We present a system for multilingual information retrieval that allows users to formulate queries in their preferred language and retrieve relevant information from a collection containing documents in multiple languages. The system is based on a process of document level alignments, where documents of different languages are paired according to their similarity. The resulting mapping allows us to produce a multilingual comparable corpus. Such a corpus has multiple interesting applications. It allows us to build a data structure for query translation in cross-language information retrieval (CLIR). Moreover, we also perform pseudo relevance feedback on the alignments to improve our retrieval results. And finally, multiple retrieval runs can be merged into one unified result list. The resulting system is inexpensive, adaptable to domain-specific collections and new languages and has performed very well at the TREC-7 conference CLIR system comparison. 相似文献
15.
Travis Gagie Aleksi Hartikainen Kalle Karhu Juha Kärkkäinen Gonzalo Navarro Simon J. Puglisi Jouni Sirén 《Information Retrieval》2017,20(3):253-291
Most of the fastest-growing string collections today are repetitive, that is, most of the constituent documents are similar to many others. As these collections keep growing, a key approach to handling them is to exploit their repetitiveness, which can reduce their space usage by orders of magnitude. We study the problem of indexing repetitive string collections in order to perform efficient document retrieval operations on them. Document retrieval problems are routinely solved by search engines on large natural language collections, but the techniques are less developed on generic string collections. The case of repetitive string collections is even less understood, and there are very few existing solutions. We develop two novel ideas, interleaved LCPs and precomputed document lists, that yield highly compressed indexes solving the problem of document listing (find all the documents where a string appears), top-k document retrieval (find the k documents where a string appears most often), and document counting (count the number of documents where a string appears). We also show that a classical data structure supporting the latter query becomes highly compressible on repetitive data. Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive multi-term queries under the simple \({\textsf{tf}}{\textsf{-}}{\textsf{idf}}\) model of relevance. We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and recommend the best choices for each case. 相似文献
16.
Content-only queries in hierarchically structured documents should retrieve the most specific document nodes which are exhaustive
to the information need. For this problem, we investigate two methods of augmentation, which both yield high retrieval quality.
As retrieval effectiveness, we consider the ratio of retrieval quality and response time; thus, fast approximations to the
'correct' retrieval result may yield higher effectiveness. We present a classification scheme for algorithms addressing this
issue, and adopt known algorithms from standard document retrieval for XML retrieval. As a new strategy, we propose incremental-interruptible retrieval, which allows for instant presentation of the top ranking documents. We develop a new algorithm implementing this strategy
and evaluate the different methods with the INEX collection. 相似文献
17.
交互式跨语言信息检索是信息检索的一个重要分支。在分析交互式跨语言信息检索过程、评价指标、用户行为进展等理论研究基础上,设计一个让用户参与跨语言信息检索全过程的用户检索实验。实验结果表明:用户检索词主要来自检索主题的标题;用户判断文档相关性的准确率较高;目标语言文档全文、译文摘要、译文全文都是用户认可的判断依据;翻译优化方法以及翻译优化与查询扩展的结合方法在用户交互环境下非常有效;用户对于反馈后的翻译仍然愿意做进一步选择;用户对于与跨语言信息检索系统进行交互是有需求并认可的。用户行为分析有助于指导交互式跨语言信息检索系统的设计与实践。 相似文献
18.
Smoothing of document language models is critical in language modeling approaches to information retrieval. In this paper,
we present a novel way of smoothing document language models based on propagating term counts probabilistically in a graph
of documents. A key difference between our approach and previous approaches is that our smoothing algorithm can iteratively
propagate counts and achieve smoothing with remotely related documents. Evaluation results on several TREC data sets show that the proposed method significantly outperforms the
simple collection-based smoothing method. Compared with those other smoothing methods that also exploit local corpus structures,
our method is especially effective in improving precision in top-ranked documents through “filling in” missing query terms
in relevant documents, which is attractive since most users only pay attention to the top-ranked documents in search engine
applications.
相似文献
| ChengXiang ZhaiEmail: |
19.
Most recent document standards like XML rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. The design of such systems is still an open problem. We present a new model for structured document retrieval which allows computing scores of document parts. This model is based on Bayesian networks whose conditional probabilities are learnt from a labelled collection of structured documents—which is composed of documents, queries and their associated assessments. Training these models is a complex machine learning task and is not standard. This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion. Results are presented on the INEX corpus of XML documents. 相似文献
20.
Information Retrieval systems typically sort the result with respect to document retrieval status values (RSV). According to the Probability Ranking Principle, this ranking ensures optimum retrieval quality if the RSVs are monotonously increasing with the probabilities of relevance (as e.g. for probabilistic IR models). However, advanced applications like filtering or distributed retrieval require estimates of the actual probability of relevance. The relationship between the RSV of a document and its probability of relevance can be described by a normalisation function which maps the retrieval status value onto the probability of relevance (mapping functions). In this paper, we explore the use of linear and logistic mapping functions for different retrieval methods. In a series of upper-bound experiments, we compare the approximation quality of the different mapping functions. We also investigate the effect on the resulting retrieval quality in distributed retrieval (only merging, without resource selection). These experiments show that good estimates of the actual probability of relevance can be achieved, and that the logistic model outperforms the linear one. Retrieval quality for distributed retrieval is only slightly improved by using the logistic function. 相似文献