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1.
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
2.
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
3.
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
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5.
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
6.
This study introduces a novel framework for evaluating passage and XML retrieval. The framework focuses on a user’s effort
to localize relevant content in a result document. Measuring the effort is based on a system guided reading order of documents.
The effort is calculated as the quantity of text the user is expected to browse through. More specifically, this study seeks
evaluation metrics for retrieval methods following a specific fetch and browse approach, where in the fetch phase documents
are ranked in decreasing order according to their document score, like in document retrieval. In the browse phase, for each
retrieved document, a set of non-overlapping passages representing the relevant text within the document is retrieved. In
other words, the passages of the document are re-organized, so that the best matching passages are read first in sequential
order. We introduce an application scenario motivating the framework, and propose sample metrics based on the framework. These
metrics give a basis for the comparison of effectiveness between traditional document retrieval and passage/XML retrieval
and illuminate the benefit of passage/XML retrieval. 相似文献
7.
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
8.
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
9.
Shiva Imani Moghadasi Sri Devi Ravana Sudharshan N. Raman 《Journal of Informetrics》2013,7(2):301-312
For a system-based information retrieval evaluation, test collection model still remains as a costly task. Producing relevance judgments is an expensive, time consuming task which has to be performed by human assessors. It is not viable to assess the relevancy of every single document in a corpus against each topic for a large collection. In an experimental-based environment, partial judgment on the basis of a pooling method is created to substitute a complete assessment of documents for relevancy. Due to the increasing number of documents, topics, and retrieval systems, the need to perform low-cost evaluations while obtaining reliable results is essential. Researchers are seeking techniques to reduce the costs of experimental IR evaluation process by the means of reducing the number of relevance judgments to be performed or even eliminating them while still obtaining reliable results. In this paper, various state-of-the-art approaches in performing low-cost retrieval evaluation are discussed under each of the following categories; selecting the best sets of documents to be judged; calculating evaluation measures, both, robust to incomplete judgments; statistical inference of evaluation metrics; inference of judgments on relevance, query selection; techniques to test the reliability of the evaluation and reusability of the constructed collections; and other alternative methods to pooling. This paper is intended to link the reader to the corpus of ‘must read’ papers in the area of low-cost evaluation of IR systems. 相似文献
10.
Multilingual information retrieval is generally understood to mean the retrieval of relevant information in multiple target
languages in response to a user query in a single source language. In a multilingual federated search environment, different
information sources contain documents in different languages. A general search strategy in multilingual federated search environments
is to translate the user query to each language of the information sources and run a monolingual search in each information
source. It is then necessary to obtain a single ranked document list by merging the individual ranked lists from the information
sources that are in different languages. This is known as the results merging problem for multilingual information retrieval.
Previous research has shown that the simple approach of normalizing source-specific document scores is not effective. On the
other side, a more effective merging method was proposed to download and translate all retrieved documents into the source
language and generate the final ranked list by running a monolingual search in the search client. The latter method is more
effective but is associated with a large amount of online communication and computation costs. This paper proposes an effective
and efficient approach for the results merging task of multilingual ranked lists. Particularly, it downloads only a small
number of documents from the individual ranked lists of each user query to calculate comparable document scores by utilizing
both the query-based translation method and the document-based translation method. Then, query-specific and source-specific
transformation models can be trained for individual ranked lists by using the information of these downloaded documents. These
transformation models are used to estimate comparable document scores for all retrieved documents and thus the documents can
be sorted into a final ranked list. This merging approach is efficient as only a subset of the retrieved documents are downloaded
and translated online. Furthermore, an extensive set of experiments on the Cross-Language Evaluation Forum (CLEF) () data has demonstrated the effectiveness of the query-specific and source-specific results merging algorithm against other
alternatives. The new research in this paper proposes different variants of the query-specific and source-specific results
merging algorithm with different transformation models. This paper also provides thorough experimental results as well as
detailed analysis. All of the work substantially extends the preliminary research in (Si and Callan, in: Peters (ed.) Results
of the cross-language evaluation forum-CLEF 2005, 2005).
相似文献
| Hao YuanEmail: |
11.
提出隐性目集的概念，并通过列举某些检索系统中的隐性条目集，探讨如何利用它们进行综合型检索，提高检索效率或进行特殊的检索，最后探讨自综合型检索的方法 相似文献
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13.
文章对ISO、IEC、ITU、CEN等主要国际标准文献检索平台进行了系统调研,并从收录内容、检索方式、著录方式及检索结果四个方面作了分析、评价与比较,以便用户选择使用。最后,针对我国标准检索平台重质量轻数量、检索字段少、检准率和检全率低、收录不全、更新慢等不足给出了相关建议,包括提高著录质量、增加检索字段、提供多语言检索和检索结果的多种排序方式、提高时效性和连续性、整合标准文献。 相似文献
14.
15.
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
16.
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
17.
文献推荐系统：提高信息检索效率之途 总被引：2，自引：0，他引：2
Traditional Information Retrieval (IR) systems have limitations in improving search performance in today’s information environment. The high recall and poor precision of traditional IR systems are only as good as with the accuracy of search query, which is, however, usually difficult for the user to construct. It is also time-consuming for the user to evaluate each search result. The recommendation techniques having been developed since the early 1990s help solve the problems that traditional IR systems have. This paper explains the basic process and major elements of document recommender systems, especially the two recommendation techniques of content-based filtering and collaborative filtering. Also discussed are the evaluation issue and the problems that current document recommender systems are facing, which need to be taken into account in future system designs. Traditional Information Retrieval (IR) systems have limitations in improving search performance in today’s information environment. The high recall and poor precision of traditional IR systems are only as good as with the accuracy of search query, which is, however, usually difficult for the user to construct. It is also time-consuming for the user to evaluate each search result. The recommendation techniques having been developed since the early 1990s help solve the problems that traditional IR systems have. This paper explains the basic process and major elements of document recommender systems, especially the two recommendation techniques of content-based filtering and collaborative filtering. Also discussed are the evaluation issue and the problems that current document recommender systems are facing, which need to be taken into account in future system designs. 相似文献
18.
Document clustering of scientific texts using citation contexts 总被引：3，自引：0，他引：3
Document clustering has many important applications in the area of data mining and information retrieval. Many existing document
clustering techniques use the “bag-of-words” model to represent the content of a document. However, this representation is
only effective for grouping related documents when these documents share a large proportion of lexically equivalent terms.
In other words, instances of synonymy between related documents are ignored, which can reduce the effectiveness of applications
using a standard full-text document representation. To address this problem, we present a new approach for clustering scientific
documents, based on the utilization of citation contexts. A citation context is essentially the text surrounding the reference
markers used to refer to other scientific works. We hypothesize that citation contexts will provide relevant synonymous and
related vocabulary which will help increase the effectiveness of the bag-of-words representation. In this paper, we investigate
the power of these citation-specific word features, and compare them with the original document’s textual representation in
a document clustering task on two collections of labeled scientific journal papers from two distinct domains: High Energy
Physics and Genomics. We also compare these text-based clustering techniques with a link-based clustering algorithm which
determines the similarity between documents based on the number of co-citations, that is in-links represented by citing documents
and out-links represented by cited documents. Our experimental results indicate that the use of citation contexts, when combined
with the vocabulary in the full-text of the document, is a promising alternative means of capturing critical topics covered
by journal articles. More specifically, this document representation strategy when used by the clustering algorithm investigated
in this paper, outperforms both the full-text clustering approach and the link-based clustering technique on both scientific
journal datasets. 相似文献
19.
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
20.
Fusion Via a Linear Combination of Scores 总被引：9，自引：2，他引：7
We present a thorough analysis of the capabilities of the linear combination (LC) model for fusion of information retrieval systems. The LC model combines the results lists of multiple IR systems by scoring each document using a weighted sum of the scores from each of the component systems. We first present both empirical and analytical justification for the hypotheses that such a model should only be used when the systems involved have high performance, a large overlap of relevant documents, and a small overlap of nonrelevant documents. The empirical approach allows us to very accurately predict the performance of a combined system. We also derive a formula for a theoretically optimal weighting scheme for combining 2 systems. We introduce d—the difference between the average score on relevant documents and the average score on nonrelevant documents—as a performance measure which not only allows mathematical reasoning about system performance, but also allows the selection of weights which generalize well to new documents. We describe a number of experiments involving large numbers of different IR systems which support these findings. 相似文献