共查询到20条相似文献，搜索用时 188 毫秒
1.
Avi Arampatzis Konstantinos Zagoris Savvas A. Chatzichristofis 《Information processing & management》2013
Content-based image retrieval (CBIR) with global features is notoriously noisy, especially for image queries with low percentages of relevant images in a collection. Moreover, CBIR typically ranks the whole collection, which is inefficient for large databases. We experiment with a method for image retrieval from multimedia databases, which improves both the effectiveness and efficiency of traditional CBIR by exploring secondary media. We perform retrieval in a two-stage fashion: first rank by a secondary medium, and then perform CBIR only on the top-K items. Thus, effectiveness is improved by performing CBIR on a ‘better’ subset. Using a relatively ‘cheap’ first stage, efficiency is also improved via the fewer CBIR operations performed. Our main novelty is that K is dynamic, i.e. estimated per query to optimize a predefined effectiveness measure. We show that our dynamic two-stage method can be significantly more effective and robust than similar setups with static thresholds previously proposed. In additional experiments using local feature derivatives in the visual stage instead of global, such as the emerging visual codebook approach, we find that two-stage does not work very well. We attribute the weaker performance of the visual codebook to the enhanced visual diversity produced by the textual stage which diminishes codebook’s advantage over global features. Furthermore, we compare dynamic two-stage retrieval to traditional score-based fusion of results retrieved visually and textually. We find that fusion is also significantly more effective than single-medium baselines. Although, there is no clear winner between two-stage and fusion, the methods exhibit different robustness features; nevertheless, two-stage retrieval provides efficiency benefits over fusion. 相似文献
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Term weighting for document ranking and retrieval has been an important research topic in information retrieval for decades. We propose a novel term weighting method based on a hypothesis that a term’s role in accumulated retrieval sessions in the past affects its general importance regardless. It utilizes availability of past retrieval results consisting of the queries that contain a particular term, retrieved documents, and their relevance judgments. A term’s evidential weight, as we propose in this paper, depends on the degree to which the mean frequency values for the relevant and non-relevant document distributions in the past are different. More precisely, it takes into account the rankings and similarity values of the relevant and non-relevant documents. Our experimental result using standard test collections shows that the proposed term weighting scheme improves conventional TF*IDF and language model based schemes. It indicates that evidential term weights bring in a new aspect of term importance and complement the collection statistics based on TF*IDF. We also show how the proposed term weighting scheme based on the notion of evidential weights are related to the well-known weighting schemes based on language modeling and probabilistic models. 相似文献
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Information seeking is traditionally conducted in environments where search results are represented at the user interface by a minimal amount of meta-information such as titles and query-based summaries. The goal of this form of presentation is to give searchers sufficient context to help them make informed interaction decisions without overloading them cognitively. The principle of polyrepresentation [Ingwersen, P. (1996). Cognitive perspectives of information retrieval interaction: elements of a cognitive IR theory. Journal of Documentation 52, 3–50] suggests that information retrieval (IR) systems should provide and use different cognitive structures during acts of communication to reduce the uncertainty associated with interactive IR. In previous work we have created content-rich search interfaces that implement an aspect of polyrepresentative theory, and are capable of displaying multiple representations of the retrieved documents simultaneously at the results interface. Searcher interaction with content-rich interfaces was used as implicit relevance feedback (IRF) to construct modified queries. These interfaces have been shown to be successful in experimentation with human subjects but we do not know whether the information was presented in a way that makes good use of the display space, or positioned most useful components in easily accessible locations, for use in IRF. In this article we use simulations of searcher interaction behaviour as design tools to determine the most rational interface design for when IRF is employed. This research forms part of the iterative design of interfaces to proactively support searchers. 相似文献
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In this paper results from three studies examining 1295 relevance judgments by 36 information retrieval (IR) system end-users is reported. Both the region of the relevance judgments, from non-relevant to highly relevant, and the motivations or levels for the relevance judgments are examined. Three major findings are studied. First, the frequency distributions of relevance judgments by IR system end-users tend to take on a bi-modal shape with peaks at the extremes (non-relevant/relevant) with a flatter middle range. Second, the different type of scale (interval or ordinal) used in each study did not alter the shape of the relevance frequency distributions. And third, on an interval scale, the median point of relevance judgment distributions correlates with the point where relevant and partially relevant items begin to be retrieved. The median point of a distribution of relevance judgments may provide a measure of user/IR system interaction to supplement precision/recall measures. The implications of investigation for relevance theory and IR systems evaluation are discussed. 相似文献
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Interdocument similarities are the fundamental information source required in cluster-based retrieval, which is an advanced retrieval approach that significantly improves performance during information retrieval (IR). An effective similarity metric is query-sensitive similarity, which was introduced by Tombros and Rijsbergen as method to more directly satisfy the cluster hypothesis that forms the basis of cluster-based retrieval. Although this method is reported to be effective, existing applications of query-specific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based on probabilistic co-relevance, where the similarity between two documents is proportional to the probability that they are both co-relevant to a specific given query. We further simplify the proposed co-relevance-based similarity by decomposing it into two separate relevance models. We then formulate all the requisite components for the proposed similarity metric in terms of scoring functions used by language modeling methods. Experimental results obtained using standard TREC test collections consistently showed that the proposed query-sensitive similarity measure performs better than term-based similarity and existing query-sensitive similarity in the context of Voorhees’ nearest neighbor test (NNT). 相似文献
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This paper presents a relevance model to rank the facts of a data warehouse that are described in a set of documents retrieved with an information retrieval (IR) query. The model is based in language modeling and relevance modeling techniques. We estimate the relevance of the facts by the probability of finding their dimensions values and the query keywords in the documents that are relevant to the query. The model is the core of the so-called contextualized warehouse, which is a new kind of decision support system that combines structured data sources and document collections. The paper evaluates the relevance model with the Wall Street Journal (WSJ) TREC test subcollection and a self-constructed fact database. 相似文献
7.
Fatemeh Lashkari Ebrahim Bagheri Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755
Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. 相似文献
8.
Egghe’s three papers regarding the universal IR surface (2004, 2007, 2008) clearly represent an original and significant contribution to the IR evaluation literature. However, Egghe’s attempt to find a complete set of universal IR evaluation points (P,R,F,M) fell short of his goal: his universal IR surface equation did not suffice in and of itself, and his continuous extension argument was insufficient to find all the remaining points (quadruples). Egghe found only two extra universal IR evaluation points, (1,1,0,0) and (0,0,1,1), but it turns out that a total of 15 additional, valid, universal IR evaluation points exist. The gap first appeared in Egghe’s earliest paper and was carried into subsequent papers. The mathematical method used here for finding the additional universal IR evaluation points involves defining the relevance metrics P,R,F,M in terms of the Swets variables a,b,c,d. Then the maximum possible number of additional quadruples is deduced, and finally, all the invalid quadruples are eliminated so that only the valid, universal IR points remain. Six of these points may be interpreted as being continuous extensions of the universal IR surface, while the other nine points may be interpreted as being “off the universal IR surface.” This completely solves the problem of finding the maximum range possible of universal IR evaluation points. 相似文献
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Emmanouil Amolochitis Ioannis T. Christou Zheng-Hua Tan Ramjee Prasad 《Information processing & management》2013
We present PubSearch, a hybrid heuristic scheme for re-ranking academic papers retrieved from standard digital libraries such as the ACM Portal. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper’s index terms with each other. We designed and developed a meta-search engine that submits user queries to standard digital repositories of academic publications and re-ranks the repository results using the hierarchical heuristic scheme. We evaluate our proposed re-ranking scheme via user feedback against the results of ACM Portal on a total of 58 different user queries specified from 15 different users. The results show that our proposed scheme significantly outperforms ACM Portal in terms of retrieval precision as measured by most common metrics in Information Retrieval including Normalized Discounted Cumulative Gain (NDCG), Expected Reciprocal Rank (ERR) as well as a newly introduced lexicographic rule (LEX) of ranking search results. In particular, PubSearch outperforms ACM Portal by more than 77% in terms of ERR, by more than 11% in terms of NDCG, and by more than 907.5% in terms of LEX. We also re-rank the top-10 results of a subset of the original 58 user queries produced by Google Scholar, Microsoft Academic Search, and ArnetMiner; the results show that PubSearch compares very well against these search engines as well. The proposed scheme can be easily plugged in any existing search engine for retrieval of academic publications. 相似文献
12.
The estimation of query model is an important task in language modeling (LM) approaches to information retrieval (IR). The ideal estimation is expected to be not only effective in terms of high mean retrieval performance over all queries, but also stable in terms of low variance of retrieval performance across different queries. In practice, however, improving effectiveness can sacrifice stability, and vice versa. In this paper, we propose to study this tradeoff from a new perspective, i.e., the bias–variance tradeoff, which is a fundamental theory in statistics. We formulate the notion of bias–variance regarding retrieval performance and estimation quality of query models. We then investigate several estimated query models, by analyzing when and why the bias–variance tradeoff will occur, and how the bias and variance can be reduced simultaneously. A series of experiments on four TREC collections have been conducted to systematically evaluate our bias–variance analysis. Our approach and results will potentially form an analysis framework and a novel evaluation strategy for query language modeling. 相似文献
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《Information processing & management》2020,57(4):102240
Multi-label classification (MLC) has attracted many researchers in the field of machine learning as it has a straightforward problem statement with varied solution approaches. Multi-label classifiers predict multiple labels for a single instance. The problem becomes challenging with the increasing number of features, especially when there are many features and labels which depend on each other. It requires dimensionality reduction before applying any multi-label learning method. This paper introduces a method named FS-MLC (Feature Selection forMulti-Label classification using Clustering in feature-space). It is a wrapper feature selection method that uses clustering to find the similarity among features and example-based precision and recall as the metrics for feature rankings to improve the performance of the associated classifier in terms of sample-based measures. First, clusters are created for features considering them as instances then features from different clusters are selected as the representative of all the features for that cluster. It reduces the number of features as a single feature represents multiple features within a cluster. It neither requires any parameter tuning nor the user threshold for the number of features selected. Extensive experimentation is performed to evaluate the efficacy of these reduced features using nine benchmark MLC datasets on twelve performance measures. The results show an impressive improvement in terms of sample-based precision, recall, and f1-score with up to 23%-93% discarded features. 相似文献
15.
Rocchio relevance feedback and latent semantic indexing (LSI) are well-known extensions of the vector space model for information retrieval (IR). This paper analyzes the statistical relationship between these extensions. The analysis focuses on each method’s basis in least-squares optimization. Noting that LSI and Rocchio relevance feedback both alter the vector space model in a way that is in some sense least-squares optimal, we ask: what is the relationship between LSI’s and Rocchio’s notions of optimality? What does this relationship imply for IR? Using an analytical approach, we argue that Rocchio relevance feedback is optimal if we understand retrieval as a simplified classification problem. On the other hand, LSI’s motivation comes to the fore if we understand it as a biased regression technique, where projection onto a low-dimensional orthogonal subspace of the documents reduces model variance. 相似文献
16.
This paper studies mathematical properties of h-index sequences as developed by Liang [Liang, L. (2006). h-Index sequence and h-index matrix: Constructions and applications. Scientometrics,69(1), 153–159]. For practical reasons, Liming studies such sequences where the time goes backwards while it is more logical to use the time going forward (real career periods). Both type of h-index sequences are studied here and their interrelations are revealed. We show cases where these sequences are convex, linear and concave. We also show that, when one of the sequences is convex then the other one is concave, showing that the reverse-time sequence, in general, cannot be used to derive similar properties of the (difficult to obtain) forward time sequence. We show that both sequences are the same if and only if the author produces the same number of papers per year. If the author produces an increasing number of papers per year, then Liang’s h-sequences are above the “normal” ones. All these results are also valid for g- and R-sequences. The results are confirmed by the h-, g- and R-sequences (forward and reverse time) of the author. 相似文献
17.
n-grams have been used widely and successfully for approximate string matching in many areas. s-grams have been introduced recently as an n-gram based matching technique, where di-grams are formed of both adjacent and non-adjacent characters. s-grams have proved successful in approximate string matching across language boundaries in Information Retrieval (IR). s-grams however lack precise definitions. Also their similarity comparison lacks precise definition. In this paper, we give precise definitions for both. Our definitions are developed in a bottom-up manner, only assuming character strings and elementary mathematical concepts. Extending established practices, we provide novel definitions of s-gram profiles and the L1 distance metric for them. This is a stronger string proximity measure than the popular Jaccard similarity measure because Jaccard is insensitive to the counts of each n-gram in the strings to be compared. However, due to the popularity of Jaccard in IR experiments, we define the reduction of s-gram profiles to binary profiles in order to precisely define the (extended) Jaccard similarity function for s-grams. We also show that n-gram similarity/distance computations are special cases of our generalized definitions. 相似文献
18.
The questionnaire is an important technique for gathering data from subjects during interactive information retrieval (IR) experiments. Research in survey methodology, public opinion polling and psychology has demonstrated a number of response biases and behaviors that subjects exhibit when responding to questionnaires. Furthermore, research in human–computer interaction has demonstrated that subjects tend to inflate their ratings of systems when completing usability questionnaires. In this study we investigate the relationship between questionnaire mode and subjects’ responses to a usability questionnaire comprised of closed and open questions administered during an interactive IR experiment. Three questionnaire modes (pen-and-paper, electronic and interview) were explored with 51 subjects who used one of two information retrieval systems. Results showed that subjects’ quantitative evaluations of systems were significantly lower in the interview mode than in the electronic mode. With respect to open questions, subjects in the interview mode used significantly more words than subjects in the pen-and-paper or electronic modes to communicate their responses, and communicated a significantly higher number of response units, even though the total number of unique response units was roughly the same across condition. Finally, results showed that subjects in the pen-and-paper mode were the most efficient in communicating their responses to open questions. These results suggest that researchers should use the interview mode to elicit responses to closed questions from subjects and either pen-and-paper or electronic modes to elicit responses to open questions. 相似文献
19.
This paper presents an investigation about how to automatically formulate effective queries using full or partial relevance information (i.e., the terms that are in relevant documents) in the context of relevance feedback (RF). The effects of adding relevance information in the RF environment are studied via controlled experiments. The conditions of these controlled experiments are formalized into a set of assumptions that form the framework of our study. This framework is called idealized relevance feedback (IRF) framework. In our IRF settings, we confirm the previous findings of relevance feedback studies. In addition, our experiments show that better retrieval effectiveness can be obtained when (i) we normalize the term weights by their ranks, (ii) we select weighted terms in the top K retrieved documents, (iii) we include terms in the initial title queries, and (iv) we use the best query sizes for each topic instead of the average best query size where they produce at most five percentage points improvement in the mean average precision (MAP) value. We have also achieved a new level of retrieval effectiveness which is about 55–60% MAP instead of 40+% in the previous findings. This new level of retrieval effectiveness was found to be similar to a level using a TREC ad hoc test collection that is about double the number of documents in the TREC-3 test collection used in previous works. 相似文献
20.
Concurrent concepts of specificity are discussed and differentiated from each other to investigate the relationship between index term specificity and users’ relevance judgments. The identified concepts are term-document specificity, hierarchical specificity, statement specificity, and posting specificity. Among them, term-document specificity, which is a relationship between an index term and the document indexed with the term, is regarded as a fruitful research area. In an experiment involving three searches with 175 retrieved documents from 356 matched index terms, the impact of specificity on relevance judgments is analyzed and found to be statistically significant. Implications for index practice and for future research are discussed. 相似文献