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1.
We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as "bag of concepts" that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall. 相似文献
2.
《Information processing & management》2022,59(5):103026
Pseudo-relevance feedback (PRF) is a classical technique to improve search engine retrieval effectiveness, by closing the vocabulary gap between users’ query formulations and the relevant documents. While PRF is typically applied on the same target corpus as the final retrieval, in the past, external expansion techniques have sometimes been applied to obtain a high-quality pseudo-relevant feedback set using the external corpus. However, such external expansion approaches have only been studied for sparse (BoW) retrieval methods, and its effectiveness for recent dense retrieval methods remains under-investigated. Indeed, dense retrieval approaches such as ANCE and ColBERT, which conduct similarity search based on encoded contextualised query and document embeddings, are of increasing importance. Moreover, pseudo-relevance feedback mechanisms have been proposed to further enhance dense retrieval effectiveness. In particular, in this work, we examine the application of dense external expansion to improve zero-shot retrieval effectiveness, i.e. evaluation on corpora without further training. Zero-shot retrieval experiments with six datasets, including two TREC datasets and four BEIR datasets, when applying the MSMARCO passage collection as external corpus, indicate that obtaining external feedback documents using ColBERT can significantly improve NDCG@10 for the sparse retrieval (by upto 28%) and the dense retrieval (by upto 12%). In addition, using ANCE on the external corpus brings upto 30% NDCG@10 improvements for the sparse retrieval and upto 29% for the dense retrieval. 相似文献
3.
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
4.
Many of the approaches to image retrieval on the Web have their basis in text retrieval. However, when searchers are asked to describe their image needs, the resulting query is often short and potentially ambiguous. The solution we propose is to perform automatic query expansion using Wikipedia as the source knowledge base, resulting in a diversification of the search results. The outcome is a broad range of images that represent the various possible interpretations of the query. In order to assist the searcher in finding images that match their specific intentions for the query, we have developed an image organization method that uses both the conceptual information associated with each image, and the visual features extracted from the images. This, coupled with a hierarchical organization of the concepts, provides an interactive interface that takes advantage of the searchers’ abilities to recognize relevant concepts, filter and focus the search results based on these concepts, and visually identify relevant images while navigating within the image space. In this paper, we outline the key features of our image retrieval system (CIDER), and present the results of a preliminary user evaluation. The results of this study illustrate the potential benefits that CIDER can provide for searchers conducting image retrieval tasks. 相似文献
5.
《Information processing & management》2020,57(6):102356
Diversification of web search results aims to promote documents with diverse content (i.e., covering different aspects of a query) to the top-ranked positions, to satisfy more users, enhance fairness and reduce bias. In this work, we focus on the explicit diversification methods, which assume that the query aspects are known at the diversification time, and leverage supervised learning methods to improve their performance in three different frameworks with different features and goals. First, in the LTRDiv framework, we focus on applying typical learning to rank (LTR) algorithms to obtain a ranking where each top-ranked document covers as many aspects as possible. We argue that such rankings optimize various diversification metrics (under certain assumptions), and hence, are likely to achieve diversity in practice. Second, in the AspectRanker framework, we apply LTR for ranking the aspects of a query with the goal of more accurately setting the aspect importance values for diversification. As features, we exploit several pre- and post-retrieval query performance predictors (QPPs) to estimate how well a given aspect is covered among the candidate documents. Finally, in the LmDiv framework, we cast the diversification problem into an alternative fusion task, namely, the supervised merging of rankings per query aspect. We again use QPPs computed over the candidate set for each aspect, and optimize an objective function that is tailored for the diversification goal. We conduct thorough comparative experiments using both the basic systems (based on the well-known BM25 matching function) and the best-performing systems (with more sophisticated retrieval methods) from previous TREC campaigns. Our findings reveal that the proposed frameworks, especially AspectRanker and LmDiv, outperform both non-diversified rankings and two strong diversification baselines (i.e., xQuAD and its variant) in terms of various effectiveness metrics. 相似文献
6.
Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities. 相似文献
7.
8.
Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments. 相似文献
9.
《Information processing & management》2005,41(5):1163-1173
One of the major problems in information retrieval is the formulation of queries on the part of the user. This entails specifying a set of words or terms that express their informational need. However, it is well-known that two people can assign different terms to refer to the same concepts. The techniques that attempt to reduce this problem as much as possible generally start from a first search, and then study how the initial query can be modified to obtain better results. In general, the construction of the new query involves expanding the terms of the initial query and recalculating the importance of each term in the expanded query. Depending on the technique used to formulate the new query several strategies are distinguished. These strategies are based on the idea that if two terms are similar (with respect to any criterion), the documents in which both terms appear frequently will also be related. The technique we used in this study is known as query expansion using similarity thesauri. 相似文献
10.
We propose in this paper an architecture for near-duplicate video detection based on: (i) index and query signature based structures integrating temporal and perceptual visual features and (ii) a matching framework computing the logical inference between index and query documents. As far as indexing is concerned, instead of concatenating low-level visual features in high-dimensional spaces which results in curse of dimensionality and redundancy issues, we adopt a perceptual symbolic representation based on color and texture concepts. For matching, we propose to instantiate a retrieval model based on logical inference through the coupling of an N-gram sliding window process and theoretically-sound lattice-based structures. The techniques we cover are robust and insensitive to general video editing and/or degradation, making it ideal for re-broadcasted video search. Experiments are carried out on large quantities of video data collected from the TRECVID 02, 03 and 04 collections and real-world video broadcasts recorded from two German TV stations. An empirical comparison over two state-of-the-art dynamic programming techniques is encouraging and demonstrates the advantage and feasibility of our method. 相似文献
11.
Most existing search engines focus on document retrieval. However, information needs are certainly not limited to finding relevant documents. Instead, a user may want to find relevant entities such as persons and organizations. In this paper, we study the problem of related entity finding. Our goal is to rank entities based on their relevance to a structured query, which specifies an input entity, the type of related entities and the relation between the input and related entities. We first discuss a general probabilistic framework, derive six possible retrieval models to rank the related entities, and then compare these models both analytically and empirically. To further improve performance, we study the problem of feedback in the context of related entity finding. Specifically, we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities. Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models. 相似文献
12.
《Information processing & management》2005,41(3):457-473
Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in an approach often called structured query translation. In contrast, language models incorporate translation probabilities into a unified framework. We compare the two approaches on Arabic and Spanish data sets, using two kinds of bilingual dictionaries––one derived from a conventional dictionary, and one derived from a parallel corpus. We find that structured query processing gives slightly better results when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus.We pursue two additional issues inherent in the comparison of structured query processing with language modeling. The first concerns query expansion, and the second is the role of translation probabilities. We compare conventional expansion techniques (pseudo-relevance feedback) with relevance modeling, a new IR approach which fits into the formal framework of language modeling. We find that relevance modeling and pseudo-relevance feedback achieve comparable levels of retrieval and that good translation probabilities confer a small but significant advantage. 相似文献
13.
Interactive query expansion (IQE) (c.f. [Efthimiadis, E. N. (1996). Query expansion. Annual Review of Information Systems and Technology, 31, 121–187]) is a potentially useful technique to help searchers formulate improved query statements, and ultimately retrieve better search results. However, IQE is seldom used in operational settings. Two possible explanations for this are that IQE is generally not integrated into searchers’ established information-seeking behaviors (e.g., examining lists of documents), and it may not be offered at a time in the search when it is needed most (i.e., during the initial query formulation). These challenges can be addressed by coupling IQE more closely with familiar search activities, rather than as a separate functionality that searchers must learn. In this article we introduce and evaluate a variant of IQE known as Real-Time Query Expansion (RTQE). As a searcher enters their query in a text box at the interface, RTQE provides a list of suggested additional query terms, in effect offering query expansion options while the query is formulated. To investigate how the technique is used – and when it may be useful – we conducted a user study comparing three search interfaces: a baseline interface with no query expansion support; an interface that provides expansion options during query entry, and a third interface that provides options after queries have been submitted to a search system. The results show that offering RTQE leads to better quality initial queries, more engagement in the search, and an increase in the uptake of query expansion. However, the results also imply that care must be taken when implementing RTQE interactively. Our findings have broad implications for how IQE should be offered, and form part of our research on the development of techniques to support the increased use of query expansion. 相似文献
14.
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
15.
Jamal Abdul Nasir Iraklis Varlamis Samreen Ishfaq 《Information processing & management》2019,56(5):1605-1617
Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets. 相似文献
16.
《Information processing & management》2001,37(3):521-541
We compare a user-defined passage feedback (pf) system to a document feedback (df) system. Df employed the adaptive linear model for retrieval, while pf used weighted query expansion based on positive and negative feedback. Twenty-four searchers performed the same six tasks in varying search and system-order per TREC-8 guidelines. We hypothesized that pf, which featured interactive query expansion, would outperform df, which relied on automatic query expansion. Initial analysis appeared to reject this hypothesis, as df showed slightly higher overall performance than pf. However, analysis by system-order groups indicates only the first pf use had lower performance. These data suggest that pf was more difficult to learn than df, though the second pf use yielded competitive performance. If performance of pf is indeed affected by learning, an improved pf system with usability enhancements may prove to be an effective mechanism for interactive information retrieval. 相似文献
17.
《Information processing & management》1999,35(1):19-30
In this paper, we present a comparison of collocation-based similarity measures: Jaccard, Dice and Cosine similarity measures for the proper selection of additional search terms in query expansion. In addition, we consider two more similarity measures: average conditional probability (ACP) and normalized mutual information (NMI). ACP is the mean value of two conditional probabilities between a query term and an additional search term. NMI is a normalized value of the two terms' mutual information. All these similarity measures are the functions of any two terms' frequencies and the collocation frequency, but are different in the methods of measurement. The selected measure changes the order of additional search terms and their weights, hence has a strong influence on the retrieval performance. In our experiments of query expansion using these five similarity measures, the additional search terms of Jaccard, Dice and Cosine similarity measures include more frequent terms with lower similarity values than ACP or NMI. In overall assessments of query expansion, the Jaccard, Dice and Cosine similarity measures are better than ACP and NMI in terms of retrieval effectiveness, whereas, NMI and ACP are better in terms of execution efficiency. 相似文献
18.
This paper presents a new approach to query expansion in search engines through the use of general non-topical terms (NTTs) and domain-specific semi-topical terms (STTs). NTTs and STTs can be used in conjunction with topical terms (TTs) to improve precision in retrieval results. In Phase I, 20 topical queries in two domains (Health and the Social Sciences) were carried out in Google and from the results of the queries, 800 pages were textually analysed. Of 1442 NTTs and STTs identified, 15% were shared between the two domains; 62% were NTTs and 38% were STTs; and approximately 64% occurred before while 36% occurred after their respective topical terms (TTs). Findings of Phase II showed that query expansion through NTTs (or STTs) particularly in the ‘exact title’ and URL search options resulted in more precise and manageable results. Statistically significant differences were found between Health and the Social Sciences vis-à-vis keyword and ‘exact phrase’ search results; however there were no significant differences in exact title and URL search results. The ratio of exact phrase, exact title, and URL search result frequencies to keyword search result frequencies also showed statistically significant differences between the two domains. Our findings suggest that web searching could be greatly enhanced combining NTTs (and STTs) with TTs in an initial query. Additionally, search results would improve if queries are restricted to the exact title or URL search options. Finally, we suggest the development and implementation of knowledge-based lists of NTTs (and STTs) by both general and specialized search engines to aid query expansion. 相似文献
19.
As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and translingual information retrieval (TLIR). The studies of RF in TLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in TLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolving some out-of-vocabulary terms. In this paper, we propose a novel relevance feedback method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra available translation alternatives so that the translated queries are more tuned to the current search. We studied TE using pseudo-relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve TLIR with both types of relevance feedback methods, and that the improvement is comparable to that of query expansion. More importantly, the effects of translation enhancement and query expansion are complementary. Their integration can produce further improvement, and makes TLIR more robust for a variety of queries. 相似文献
20.
The term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on query expansion and cluster-based retrieval. We examine the effect of using parsimony in query expansion and the effect of clustering algorithms in cluster-based retrieval. In addition, query expansion and cluster-based retrieval are compared, and their combinations are evaluated in terms of retrieval performance by performing experimentations on seven test collections of NTCIR and TREC. 相似文献