共查询到20条相似文献，搜索用时 687 毫秒
1.
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
2.
This paper presents four novel techniques for open-vocabulary spoken document retrieval: a method to detect slots that possibly contain a query feature; a method to estimate occurrence probabilities; a technique that we call collection-wide probability re-estimation and a weighting scheme which takes advantage of the fact that long query features are detected more reliably. These four techniques have been evaluated using the TREC-6 spoken document retrieval test collection to determine the improvements in retrieval effectiveness with respect to a baseline retrieval method. Results show that the retrieval effectiveness can be improved considerably despite the large number of speech recognition errors. 相似文献
3.
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
4.
LETOR: A benchmark collection for research on learning to rank for information retrieval 总被引：2，自引：0，他引：2
LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research
Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches.
Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how
the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation.
We then compare several state-of-the-art learning to rank algorithms on LETOR, report their ranking performances, and make
discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition
to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting
research projects on learning to rank and related topics. 相似文献
5.
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
6.
To cope with the fact that, in the ad hoc retrieval setting, documents relevant to a query could contain very few (short)
parts (passages) with query-related information, researchers proposed passage-based document ranking approaches. We show that several of
these retrieval methods can be understood, and new ones can be derived, using the same probabilistic model. We use language-model
estimates to instantiate specific retrieval algorithms, and in doing so present a novel passage language model that integrates information from the containing document to an extent controlled by the estimated document homogeneity. Several document-homogeneity measures that we present yield passage language models that are more effective than the standard
passage model for basic document retrieval and for constructing and utilizing passage-based relevance models; these relevance models also outperform a document-based relevance model. Finally, we demonstrate the merits in using the
document-homogeneity measures for integrating document-query and passage-query similarity information for document retrieval. 相似文献
7.
8.
An information retrieval (IR) system can often fail to retrieve relevant documents due to the incomplete specification of information need in the user’s query. Pseudo-relevance feedback (PRF) aims to improve IR effectiveness by exploiting potentially relevant aspects of the information need present in the documents retrieved in an initial search. Standard PRF approaches utilize the information contained in these top ranked documents from the initial search with the assumption that documents as a whole are relevant to the information need. However, in practice, documents are often multi-topical where only a portion of the documents may be relevant to the query. In this situation, exploitation of the topical composition of the top ranked documents, estimated with statistical topic modeling based approaches, can potentially be a useful cue to improve PRF effectiveness. The key idea behind our PRF method is to use the term-topic and the document-topic distributions obtained from topic modeling over the set of top ranked documents to re-rank the initially retrieved documents. The objective is to improve the ranks of documents that are primarily composed of the relevant topics expressed in the information need of the query. Our RF model can further be improved by making use of non-parametric topic modeling, where the number of topics can grow according to the document contents, thus giving the RF model the capability to adjust the number of topics based on the content of the top ranked documents. We empirically validate our topic model based RF approach on two document collections of diverse length and topical composition characteristics: (1) ad-hoc retrieval using the TREC 6-8 and the TREC Robust ’04 dataset, and (2) tweet retrieval using the TREC Microblog ’11 dataset. Results indicate that our proposed approach increases MAP by up to 9% in comparison to the results obtained with an LDA based language model (for initial retrieval) coupled with the relevance model (for feedback). Moreover, the non-parametric version of our proposed approach is shown to be more effective than its parametric counterpart due to its advantage of adapting the number of topics, improving results by up to 5.6% of MAP compared to the parametric version. 相似文献
9.
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
10.
A standard approach to Information Retrieval (IR) is to model text as a bag of words. Alternatively, text can be modelled
as a graph, whose vertices represent words, and whose edges represent relations between the words, defined on the basis of
any meaningful statistical or linguistic relation. Given such a text graph, graph theoretic computations can be applied to measure various properties of the graph, and hence of the text. This work
explores the usefulness of such graph-based text representations for IR. Specifically, we propose a principled graph-theoretic
approach of (1) computing term weights and (2) integrating discourse aspects into retrieval. Given a text graph, whose vertices
denote terms linked by co-occurrence and grammatical modification, we use graph ranking computations (e.g. PageRank Page et al.
in The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project,
1998) to derive weights for each vertex, i.e. term weights, which we use to rank documents against queries. We reason that our
graph-based term weights do not necessarily need to be normalised by document length (unlike existing term weights) because
they are already scaled by their graph-ranking computation. This is a departure from existing IR ranking functions, and we
experimentally show that it performs comparably to a tuned ranking baseline, such as BM25 (Robertson et al. in NIST Special
Publication 500-236: TREC-4, 1995). In addition, we integrate into ranking graph properties, such as the average path length, or clustering coefficient, which
represent different aspects of the topology of the graph, and by extension of the document represented as a graph. Integrating
such properties into ranking allows us to consider issues such as discourse coherence, flow and density during retrieval.
We experimentally show that this type of ranking performs comparably to BM25, and can even outperform it, across different
TREC (Voorhees and Harman in TREC: Experiment and evaluation in information retrieval, MIT Press, 2005) datasets and evaluation measures. 相似文献
11.
Christoph Baumgarten 《Information Retrieval》2000,3(3):253-271
This paper describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment.The model assumes the collection of documents offered by the environment to be partitioned into subcollections. Documents as well as subcollections have to be indexed, where indexing methods using different indexing vocabularies can be employed. A query provided by a user is answered in terms of a ranked list of documents. The model determines a procedure for ranking the documents that stems from the Probability Ranking Principle: For each subcollection, the subcollection's documents are ranked; the resulting ranked lists are combined into a final ranked list of documents, where the ordering is determined by the documents' probabilities of being relevant with respect to the user's query. Various probabilistic ranking methods may be involved in the distributed ranking process. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model.The property that different ranking methods and indexing vocabularies can be used is important when the subcollections are heterogeneous with respect to their content.The model's applicability is experimentally confirmed. When exploiting the degrees of freedom provided by the model, experiments showed evidence that the model even outperforms comparable models for the non-distributed case with respect to retrieval effectiveness. 相似文献
12.
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
13.
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: |
14.
Multilingual retrieval (querying of multiple document collections each in a different language) can be achieved by combining several individual techniques which enhance retrieval: machine translation to cross the language barrier, relevance feedback to add words to the initial query, decompounding for languages with complex term structure, and data fusion to combine monolingual retrieval results from different languages. Using the CLEF 2001 and CLEF 2002 topics and document collections, this paper evaluates these techniques within the context of a monolingual document ranking formula based upon logistic regression. Each individual technique yields improved performance over runs which do not utilize that technique. Moreover the techniques are complementary, in that combining the best techniques outperforms individual technique performance. An approximate but fast document translation using bilingual wordlists created from machine translation systems is presented and evaluated. The fast document translation is as effective as query translation in multilingual retrieval. Furthermore, when fast document translation is combined with query translation in multilingual retrieval, the performance is significantly better than that of query translation or fast document translation. 相似文献
15.
On Collection Size and Retrieval Effectiveness 总被引：3，自引：0，他引：3
The relationship between collection size and retrieval effectiveness is particularly important in the context of Web search. We investigate it first analytically and then experimentally, using samples and subsets of test collections. Different retrieval systems vary in how the score assigned to an individual document in a sample collection relates to the score it receives in the full collection; we identify four cases.We apply signal detection (SD) theory to retrieval from samples, taking into account the four cases and using a variety of shapes for relevant and irrelevant distributions. We note that the SD model subsumes several earlier hypotheses about the causes of the decreased precision in samples. We also discuss other models which contribute to an understanding of the phenomenon, particularly relating to the effects of discreteness. Different models provide complementary insights.Extensive use is made of test data, some from official submissions to the TREC-6 VLC track and some new, to illustrate the effects and test hypotheses. We empirically confirm predictions, based on SD theory, that P@n should decline when moving to a sample collection and that average precision and R-precision should remain constant. SD theory suggests the use of recall-fallout plots as operating characteristic (OC) curves. We plot OC curves of this type for a real retrieval system and query set and show that curves for sample collections are similar but not identical to the curve for the full collection. 相似文献
16.
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
17.
Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the
retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length
bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document
terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this
behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors,
and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer,
Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet
prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval
performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval
trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer
smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple
the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it
is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance
of the Jelinek–Mercer method is improved by including a length-based prior.
相似文献
| Leif AzzopardiEmail: |
18.
Olga Vechtomova 《Information Retrieval》2006,9(4):399-420
The paper presents several techniques for selecting noun phrases for interactive query expansion following pseudo-relevance
feedback and a new phrase-based document ranking method. A combined syntactico-statistical method was used for the selection
of phrases for query expansion. Several statistical measures of phrase selection were evaluated. Experiments were also conducted
studying the effectiveness of noun phrases in document ranking. One of the major problems in phrase-based document retrieval
is weighting of overlapping and non-contiguous word sequences in documents. The paper presents a new method of phrase weighting,
which addressed this problem, and its evaluation on the TREC dataset. 相似文献
19.
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
20.
Genetic Approach to Query Space Exploration 总被引：2，自引：0，他引：2
This paper describes a genetic algorithm approach for intelligent information retrieval. The goal is to find an optimal set of documents which best matches the user's needs by exploring and exploiting the document space. More precisely, we define a specific genetic algorithm for information retrieval based on knowledge based operators and guided by a heuristic for relevance multi-modality problem solving. Experiments with TREC-6 French data and queries show the effectiveness of our approach. 相似文献