Search (60 results, page 2 of 3)

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Abstract

In real-world information retrieval systems, the underlying document collection is rarely stable or definitive. This work is focused on the study of signals extracted from the content of documents at different points in time for the purpose of weighting individual terms in a document. The basic idea behind our proposals is that terms that have existed for a longer time in a document should have a greater weight. We propose 4 term weighting functions that use each document's history to estimate a current term score. To evaluate this thesis, we conduct 3 independent experiments using a collection of documents sampled from Wikipedia. In the first experiment, we use data from Wikipedia to judge each set of terms. In a second experiment, we use an external collection of tags from a popular social bookmarking service as a gold standard. In the third experiment, we crowdsource user judgments to collect feedback on term preference. Across all experiments results consistently support our thesis. We show that temporally aware measures, specifically the proposed revision term frequency and revision term frequency span, outperform a term-weighting measure based on raw term frequency alone.

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Abstract

A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques model syntagmatic associations that infer two terms co-occur more often than by chance in natural language. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches to query expansion and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process improves retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

Pseudo relevance feedback (PRF) has shown to be effective in ad hoc information retrieval. In traditional PRF methods, top-ranked documents are all assumed to be relevant and therefore treated equally in the feedback process. However, the performance gain brought by each document is different as showed in our preliminary experiments. Thus, it is more reasonable to predict the performance gain brought by each candidate feedback document in the process of PRF. We define the quality level (QL) and then use this information to adjust the weights of feedback terms in these documents. Unlike previous work, we do not make any explicit relevance assumption and we go beyond just selecting "good" documents for PRF. We propose a quality-based PRF framework, in which two quality-based assumptions are introduced. Particularly, two different strategies, relevance-based QL (RelPRF) and improvement-based QL (ImpPRF) are presented to estimate the QL of each feedback document. Based on this, we select a set of heterogeneous document-level features and apply a learning approach to evaluate the QL of each feedback document. Extensive experiments on standard TREC (Text REtrieval Conference) test collections show that our proposed model performs robustly and outperforms strong baselines significantly.

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Abstract

Biomedical decision making often requires relevant evidence from the biomedical literature. Retrieval of the evidence calls for a system that receives a natural language query for a biomedical information need and, among the huge amount of texts retrieved for the query, ranks relevant texts higher for further processing. However, state-of-the-art text rankers have weaknesses in dealing with biomedical queries, which often consist of several correlating concepts and prefer those texts that completely talk about the concepts. In this article, we present a technique, Proximity-Based Ranker Enhancer (PRE), to enhance text rankers by term-proximity information. PRE assesses the term frequency (TF) of each term in the text by integrating three types of term proximity to measure the contextual completeness of query terms appearing in nearby areas in the text being ranked. Therefore, PRE may serve as a preprocessor for (or supplement to) those rankers that consider TF in ranking, without the need to change the algorithms and development processes of the rankers. Empirical evaluation shows that PRE significantly improves various kinds of text rankers, and when compared with several state-of-the-art techniques that enhance rankers by term-proximity information, PRE may more stably and significantly enhance the rankers.

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Abstract

On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.

Content

Vgl.: doi: 10.1016/j.ipm.2013.08.007. A short preliminary version of this paper was published in the proceeding of WWW 2009 as a two page poster paper.

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Abstract

Pseudo-relevance feedback is the basis of a category of automatic query modification techniques. Pseudo-relevance feedback methods assume the initial retrieved set of documents to be relevant. Then they use these documents to extract more relevant terms for the query or just re-weigh the user's original query. In this paper, we propose a straightforward, yet effective use of pseudo-relevance feedback method in detecting more informative query terms and re-weighting them. The query-by-query analysis of our results indicates that our method is capable of identifying the most important keywords even in short queries. Our main idea is that some of the top documents may contain a closer context to the user's information need than the others. Therefore, re-examining the similarity of those top documents and weighting this set based on their context could help in identifying and re-weighting informative query terms. Our experimental results in standard English and Persian test collections show that our method improves retrieval performance, in terms of MAP criterion, up to 7% over traditional query term re-weighting methods.

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Abstract

The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).

Footnote

Beitrag in einem 'Special issue on neuro-information science'.

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Abstract

Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.

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Abstract

Question Answering is an area of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text in order to provide a more sophisticated and satisfactory response to the user's information needs. For this reason, the authors see question answering as the next step beyond standard information retrieval. In this chapter state of the art question answering is covered focusing on providing an overview of systems, techniques and approaches that are likely to be employed in the next generations of search engines. Special attention is paid to question answering using the World Wide Web as the data source and to question answering exploiting the possibilities of Semantic Web. Considerations about the current issues and prospects for promising future research are also provided.

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Abstract

In response to Hjørland's recent call for a reconceptualization of the foundations of relevance, we suggest that the sociocognitive aspects of intermediation by information agencies, such as archives and libraries, are a necessary and unexplored part of the infrastructure of the subject knowledge domains central to his recommended "view of relevance informed by a social paradigm" (2010, p. 217). From a comparative analysis of documents from 39 graduate-level introductory courses in archives, reference, and strategic/competitive intelligence taught in 13 American Library Association-accredited library and information science (LIS) programs, we identify four defining sociocognitive dimensions of "relevance work" in information agencies within Hjørland's proposed framework for relevance: tasks, time, systems, and assessors. This study is intended to supply sociocognitive content from within the relevance work domain to support further domain analytic research, and to emphasize the importance of intermediary relevance work for all subject knowledge domains.

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Abstract

This article provides a technical review of semantic search methods used to support text-based search over formal Semantic Web knowledge bases. Our focus is on ranking methods and auxiliary processes explored by existing semantic search systems, outlined within broad areas of classification. We present reflective examples from the literature in some detail, which should appeal to readers interested in a deeper perspective on the various methods and systems implemented in the outlined literature. The presentation covers graph exploration and propagation methods, adaptations of classic probabilistic retrieval models, and query-independent link analysis via flexible extensions to the PageRank algorithm. Future research directions are discussed, including development of more cohesive retrieval models to unlock further potentials and uses, data indexing schemes, integration with user interfaces, and building community consensus for more systematic evaluation and gradual development.

Series

Advances in information science

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Footnote

Beitrag in einem Special Issue "Combining bibliometrics and information retrieval"

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Abstract

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text-based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag-of-words approaches, in which the image retrieval problem turns into one of standard text-based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical-image-dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical-image-dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image-relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image-relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state-of-the-art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

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Abstract

Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification is restricted to documents belonging to clusters that potentially contain a high percentage of relevant documents. Empirical results show that the proposed framework improves the performance of several existing diversification methods. The framework also gives rise to a simple yet effective cluster-based approach to result diversification that selects documents from different clusters to be included in a ranked list in a round robin fashion. We describe a set of experiments aimed at thoroughly analyzing the behavior of the two main components of the proposed diversification framework, ranking and selecting clusters for diversification. Both components have a crucial impact on the overall performance of our framework, but ranking clusters plays a more important role than selecting clusters. We also examine properties that clusters should have in order for our diversification framework to be effective. Most relevant documents should be contained in a small number of high-quality clusters, while there should be no dominantly large clusters. Also, documents from these high-quality clusters should have a diverse content. These properties are strongly correlated with the overall performance of the proposed diversification framework.

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Abstract

Pseudorelevance feedback (PRF) was proposed to solve the limitation of relevance feedback (RF), which is based on the user-in-the-loop process. In PRF, the top-k retrieved images are regarded as PRF. Although the PRF set contains noise, PRF has proven effective for automatically improving the overall retrieval result. To implement PRF, the Rocchio algorithm has been considered as a reasonable and well-established baseline. However, the performance of Rocchio-based PRF is subject to various representation choices (or factors). In this article, we examine these factors that affect the performance of Rocchio-based PRF, including image-feature representation, the number of top-ranked images, the weighting parameters of Rocchio, and similarity measure. We offer practical insights on how to optimize the performance of Rocchio-based PRF by choosing appropriate representation choices. Our extensive experiments on NUS-WIDE-LITE and Caltech 101 + Corel 5000 data sets show that the optimal feature representation is color moment + wavelet texture in terms of retrieval efficiency and effectiveness. Other representation choices are that using top-20 ranked images as pseudopositive and pseudonegative feedback sets with the equal weight (i.e., 0.5) by the correlation and cosine distance functions can produce the optimal retrieval result.

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Abstract

Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state-of-the-art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

Implicit detection of relevance has been approached by many during the last decade. From the use of individual measures to the use of multiple features from different sources (multimodality), studies have shown the feasibility to automatically detect whether a document is relevant. Despite promising results, it is not clear yet to what extent multimodality constitutes an effective approach compared to unimodality. In this article, we hypothesize that it is possible to build unimodal models capable of outperforming multimodal models in the detection of perceived relevance. To test this hypothesis, we conducted three experiments to compare unimodal and multimodal classification models built using a combination of 24 features. Our classification experiments showed that a univariate unimodal model based on the left-click feature supports our hypothesis. On the other hand, our prediction experiment suggests that multimodality slightly improves early classification compared to the best unimodal models. Based on our results, we argue that the feasibility for practical applications of state-of-the-art multimodal approaches may be strongly constrained by technology, cultural, ethical, and legal aspects, in which case unimodality may offer a better alternative today for supporting relevance detection in interactive information retrieval systems.

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Abstract

Query translation in cross-language information retrieval (CLIR) can be done by employing dictionaries, aligned corpora, or machine translators. Scarcity of aligned corpora for various domains in many language pairs intensifies the importance of dictionary-based CLIR which motivates us to use only a bilingual dictionary and two independent collections in source and target languages for query translation. We exploit pseudo-relevant documents for a given query in the source language and pseudo-relevant documents for a translation of the query in the target language with a proposed expectation-maximization algorithm for improving query translation. The proposed method (called EM4QT) assumes that each target term either is translated from the source pseudo-relevant documents or has come from a noisy collection. Since EM4QT does not directly consider term coherency, which is defined as fluency of the target translation, we investigate a crucial question: can EM4QT be improved using either coherency-based methods or token-to-token translation ones? To address this question, we combine different translation models via simple linear interpolation and a proposed divergence minimization method. Evaluations over four CLEF collections in Persian, French, Spanish, and German indicate that EM4QT significantly outperforms competitive baselines in all the collections. Our experiments also reveal that since EM4QT indirectly considers term coherency, combining the method with coherency-based models cannot significantly improve the retrieval performance. On the other hand, investigating the query-by-query results supports the view that EM4QT usually gives a relatively high weight to one translation and its combination with the proposed token-to-token translation model, which is obtained by running EM4QT for each query term separately, soothes the effect and reaches better results for many queries. Comparing the method with a competitive word-embedding baseline reveals the superiority of the proposed model.

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Abstract

This research investigated several important issues in using implicit feedback techniques to assist searchers with difficulties in formulating effective search strategies. It focused on examining the relationship between types of behavioral evidence that can be captured from Web searches and searchers' interests. A carefully crafted observation study was conducted to capture, examine, and elucidate the analytical processes and work practices of human analysts when they simulated the role of an implicit feedback system by trying to infer searchers' interests from behavioral traces. Findings provided rare insight into the complexities and nuances in using behavioral evidence for implicit feedback and led to the proposal of an implicit feedback model for Web search that bridged previous studies on behavioral evidence and implicit feedback measures. A new level of analysis termed an analytical lens emerged from the data and provides a road map for future research on this topic.

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Abstract

In this work, we investigate the problem of using the block structure of Web pages to improve ranking results. Starting with basic intuitions provided by the concepts of term frequency (TF) and inverse document frequency (IDF), we propose nine block-weight functions to distinguish the impact of term occurrences inside page blocks, instead of inside whole pages. These are then used to compute a modified BM25 ranking function. Using four distinct Web collections, we ran extensive experiments to compare our block-weight ranking formulas with two other baselines: (a) a BM25 ranking applied to full pages, and (b) a BM25 ranking that takes into account best blocks. Our methods suggest that our block-weighting ranking method is superior to all baselines across all collections we used and that average gain in precision figures from 5 to 20% are generated.