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1.
《Information processing & management》2022,59(1):102743
Multi-modal hashing can encode the large-scale social geo-media multimedia data from multiple sources into a common discrete hash space, in which the heterogeneous correlations from multiple modalities could be well explored and preserved into the objective semantic-consistent hash codes. The current researches on multi-modal hashing mainly focus on performing common data reconstruction, but they fail to effectively distill the intrinsic and consensus structures of multi-modal data and fully exploit the inherent semantic knowledge to capture semantic-consistent information across multiple modalities, leading to unsatisfactory retrieval performance. To facilitate this problem and develop an efficient multi-modal geographical retrieval method, in this article, we propose a discriminative multi-modal hashing framework named Cognitive Multi-modal Consistent Hashing (CMCH), which can progressively pursue the structure consensus over heterogeneous multi-modal data and simultaneously explore the informative transformed semantics. Specifically, we construct a parameter-free collaborative multi-modal fusion module to incorporate and excavate the underlying common components from multi-source data. Particularly, our formulation seeks for a joint multi-modal compatibility among multiple modalities under a self-adaptive weighting manner, which can take full advantages of their complementary properties. Moreover, a cognitive self-paced learning policy is further leveraged to conduct progressive feature aggregation, which can coalesce multi-modal data onto the established common latent space in a curriculum learning mode. Furthermore, deep semantic transform learning is elaborated to generate flexible semantics for interactively guiding collaborative hash codes learning. An efficient discrete learning algorithm is devised to address the resulting optimization problem, which obtains stable solutions when dealing with large-scale multi-modal retrieval tasks. Sufficient experiments performed on four large-scale multi-modal datasets demonstrate the encouraging performance of the proposed CMCH method in comparison with the state-of-the-arts over multi-modal information retrieval and computational efficiency. The source codes of this work could be available at https://github.com/JunfengAn1998a/CMCH . 相似文献
2.
The classical probabilistic models attempt to capture the ad hoc information retrieval problem within a rigorous probabilistic framework. It has long been recognized that the primary obstacle to the effective performance of the probabilistic models is the need to estimate a relevance model. The Dirichlet compound multinomial (DCM) distribution based on the Polya Urn scheme, which can also be considered as a hierarchical Bayesian model, is a more appropriate generative model than the traditional multinomial distribution for text documents. We explore a new probabilistic model based on the DCM distribution, which enables efficient retrieval and accurate ranking. Because the DCM distribution captures the dependency of repetitive word occurrences, the new probabilistic model based on this distribution is able to model the concavity of the score function more effectively. To avoid the empirical tuning of retrieval parameters, we design several parameter estimation algorithms to automatically set model parameters. Additionally, we propose a pseudo-relevance feedback algorithm based on the mixture modeling of the Dirichlet compound multinomial distribution to further improve retrieval accuracy. Finally, our experiments show that both the baseline probabilistic retrieval algorithm based on the DCM distribution and the corresponding pseudo-relevance feedback algorithm outperform the existing language modeling systems on several TREC retrieval tasks. The main objective of this research is to develop an effective probabilistic model based on the DCM distribution. A secondary objective is to provide a thorough understanding of the probabilistic retrieval model by a theoretical understanding of various text distribution assumptions. 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.
相关概念的关联参照检索是概念检索的重要研究内容。本文提出了一种基于主题的语义关联的参照检索模型，通过融合语义网、本体论的相关知识及信息提取等语言处理技术，提取关于特定主题的文档的主题概念及概念之间的关联构成该主题的语义关联模型，并辅助于参照检索过程。 相似文献
5.
《Information processing & management》2023,60(1):103120
Multimodal fake news detection methods based on semantic information have achieved great success. However, these methods only exploit the deep features of multimodal information, which leads to a large loss of valid information at the shallow level. To address this problem, we propose a progressive fusion network (MPFN) for multimodal disinformation detection, which captures the representational information of each modality at different levels and achieves fusion between modalities at the same level and at different levels by means of a mixer to establish a strong connection between the modalities. Specifically, we use a transformer structure, which is effective in computer vision tasks, as a visual feature extractor to gradually sample features at different levels and combine features obtained from a text feature extractor and image frequency domain information at different levels for fine-grained modeling. In addition, we design a feature fusion approach to better establish connections between modalities, which can further improve the performance and thus surpass other network structures in the literature. We conducted extensive experiments on two real datasets, Weibo and Twitter, where our method achieved 83.3% accuracy on the Twitter dataset, which has increased by at least 4.3% compared to other state-of-the-art methods. This demonstrates the effectiveness of MPFN for identifying fake news, and the method reaches a relatively advanced level by combining different levels of information from each modality and a powerful modality fusion method. 相似文献
6.
《Information processing & management》2020,57(2):102165
With the rapid growth of multimedia data, cross-media hashing has gained more and more attention. However, most existing cross-modal hashing methods ignore the multi-label correlation and only apply binary similarity to measure the correlation between two instances. Most existing methods perform poorly in capturing the relevance between retrieval results and queries since binary similarity measurement has limited abilities to discriminate minor differences among different instances. In order to overcome the mentioned shortcoming, we introduce a novel notion of instance similarity method, which is used to evaluate the semantic correlation between two specific instances in training data. Base on the instance similarity, we also propose a novel deep instance hashing network, which utilizes instance similarity and binary similarity simultaneously for multi-label cross-model retrieval. The experiment results on two real datasets show the superiority of our proposed method, compared with a series of state-of-the-art cross-modal hashing methods in terms of several metric evaluations. 相似文献
7.
跨语言检索是一种重要的信息检索手段之一。为了提高跨语言检索效率,采用语义扩展的方法,通过分析其设计思想和工作流程,构建出一种基于语义扩展的跨语言自动检索模型,重点对其语义扩展、知识库和结果聚类等设计进行了阐述,提出了语义理解切分法的分词方法,采用了Single-Pass算法进行聚类,实验结果表明,该模型能有效提高跨语言检索的查全率和查准率。 相似文献
8.
基于潜在语义分析的影响自然语言检索查准率指标因素的评述 总被引：1，自引：0，他引：1
潜在语义分析是自然语言使用于情报检索系统的理论基础,以此理论建构的空间向量模型是评判检索系统性能优良与否的知识工具。阐述了潜在语义标引(LSI)的基本内容、LSI下影响自然语言检索查准率的因素及向量空间模型检索软件的运行机制。此评述对网络化的情报检索技术的发展起到了一定的参考作用。 相似文献
9.
10.
《Information processing & management》2022,59(4):102977
Information residing in multiple modalities (e.g., text, image) of social media posts can jointly provide more comprehensive and clearer insights into an ongoing emergency. To identify information valuable for humanitarian aid from noisy multimodal data, we first clarify the categories of humanitarian information, and define a multi-label multimodal humanitarian information identification task, which can adapt to the label inconsistency issue caused by modality independence while maintaining the correlation between modalities. We proposed a Multimodal Humanitarian Information Identification Model that simultaneously captures the Correlation and Independence between modalities (CIMHIM). A tailor-made dataset containing 4,383 annotated text-image pairs was built to evaluate the effectiveness of our model. The experimental results show that CIMHIM outperforms both unimodal and multimodal baseline methods by at least 0.019 in macro-F1 and 0.022 in accuracy. The combination of OCR text, object-level features, and the decision rule based on label correlations enhances the overall performance of CIMHIM. Additional experiments on a similar dataset (CrisisMMD) also demonstrate the robustness of CIMHIM. The task, model, and dataset proposed in this study contribute to the practice of leveraging multimodal social media resources to support effective emergency response. 相似文献
11.
As access to information becomes more intensive in society, a great deal of that information is becoming available through diverse channels. Accordingly, users require effective methods for accessing this information. Conversational agents can act as effective and familiar user interfaces. Although conversational agents can analyze the queries of users based on a static process, they cannot manage expressions that are more complex. In this paper, we propose a system that uses semantic Bayesian networks to infer the intentions of the user based on Bayesian networks and their semantic information. Since conversation often contains ambiguous expressions, the managing of context and uncertainty is necessary to support flexible conversational agents. The proposed method uses mixed-initiative interaction (MII) to obtain missing information and clarify spurious concepts in order to understand the intention of users correctly. We applied this to an information retrieval service for websites to verify the usefulness of the proposed method. 相似文献
12.
Question answering (QA) aims at finding exact answers to a user’s question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ranking strategy to generate the final answers. This ranking process can be challenging, as it entails identifying the relevant answers amongst many irrelevant ones. This is more challenging in multi-strategy QA, in which multiple answering agents are used to extract answer candidates. As answer candidates come from different agents with different score distributions, how to merge answer candidates plays an important role in answer ranking. In this paper, we propose a unified probabilistic framework which combines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer relevance and their correlation in answer ranking, (2) the framework supports answer merging on answer candidates returned by multiple extraction techniques, (3) the framework can support list questions as well as factoid questions, (4) the framework can be easily applied to a different QA system, and (5) the framework significantly improves performance of a QA system. An extensive set of experiments was done to support our hypotheses and demonstrate the effectiveness of the framework. All of the work substantially extends the preliminary research in Ko et al. (2007a). A probabilistic framework for answer selection in question answering. In: Proceedings of NAACL/HLT. 相似文献
13.
《Information processing & management》2023,60(3):103301
Hashing has been an emerging topic and has recently attracted widespread attention in multi-modal similarity search applications. However, most existing approaches rely on relaxation schemes to generate binary codes, leading to large quantization errors. In addition, amounts of existing approaches embed labels into the pairwise similarity matrix, leading to expensive time and space costs and losing category information. To address these issues, we propose an Efficient Discrete Matrix factorization Hashing (EDMH). Specifically, EDMH first learns the latent subspaces for individual modality through matrix factorization strategy, which preserves the semantic structure representation information of each modality. In particular, we develop a semantic label offset embedding learning strategy, improving the stability of label embedding regression. Furthermore, we design an efficient discrete optimization scheme to generate compact binary codes discretely. Eventually, we present two efficient learning strategies EDMH-L and EDMH-S to pursue high-quality hash functions. Extensive experiments on various widely-used databases verify that the proposed algorithms produce significant performance and outperform some state-of-the-art approaches, with an average improvement of 2.50% (for Wiki), 2.66% (for MIRFlickr) and 2.25% (for NUS-WIDE) over the best available results, respectively. 相似文献
14.
Shanfeng Zhu Ichigaku Takigawa Jia Zeng Hiroshi Mamitsuka 《Information processing & management》2009,45(5):555-570
We propose a new finite mixture model for clustering multiple-field documents, such as scientific literature with distinct fields: title, abstract, keywords, main text and references. This probabilistic model, which we call field independent clustering model (FICM), incorporates the distinct word distributions of each field to integrate the discriminative abilities of each field as well as to select the most suitable component probabilistic model for each field. We evaluated the performance of FICM by applying it to the problem of clustering three-field (title, abstract and MeSH) biomedical documents from TREC 2004 and 2005 Genomics tracks, and two-field (title and abstract) news reports from Reuters-21578. Experimental results showed that FICM outperformed the classical multinomial model and the multivariate Bernoulli model, being at a statistically significant level for all the three collections. These results indicate that FICM outperformed widely-used probabilistic models for document clustering by considering the characteristics of each field. We further showed that the component model, which is consistent with the nature of the corresponding field, achieved a better performance and considering the diversity of model setting also gave a further performance improvement. An extended abstract of parts of the work presented in this paper has appeared in Zhu et al. [Zhu, S., Takigawa, I., Zhang, S., & Mamitsuka, H. (2007). A probabilistic model for clustering text documents with multiple fields. In Proceedings of the 29th European conference on information retrieval, ECIR 2007. Lecture notes in computer science (Vol. 4425, pp. 331–342)]. 相似文献
15.
《Information processing & management》2020,57(6):102317
The problem of social spam detection has been traditionally modeled as a supervised classification problem. Despite the initial success of this detection approach, later analysis of proposed systems and detection features has shown that, like email spam, the dynamic and adversarial nature of social spam makes the performance achieved by supervised systems hard to maintain. In this paper, we investigate the possibility of using the output of previously proposed supervised classification systems as a tool for spammers discovery. The hypothesis is that these systems are still highly capable of detecting spammers reliably even when their recall is far from perfect. We then propose to use the output of these classifiers as prior beliefs in a probabilistic graphical model framework. This framework allows beliefs to be propagated to similar social accounts. Basing similarity on a who-connects-to-whom network has been empirically critiqued in recent literature and we propose here an alternative definition based on a bipartite users-content interaction graph. For evaluation, we build a Markov Random Field on a graph of similar users and compute prior beliefs using a selection of state-of-the-art classifiers. We apply Loopy Belief Propagation to obtain posterior predictions on users. The proposed system is evaluated on a recent Twitter dataset that we collected and manually labeled. Classification results show a significant increase in recall and a maintained precision. This validates that formulating the detection problem with an undirected graphical model framework permits to restore the deteriorated performances of previously proposed statistical classifiers and to effectively mitigate the effect of spam evolution. 相似文献
16.
《Information processing & management》2023,60(2):103145
Machine reading comprehension (MRC) is a challenging task in the field of artificial intelligence. Most existing MRC works contain a semantic matching module, either explicitly or intrinsically, to determine whether a piece of context answers a question. However, there is scant work which systematically evaluates different paradigms using semantic matching in MRC. In this paper, we conduct a systematic empirical study on semantic matching. We formulate a two-stage framework which consists of a semantic matching model and a reading model, based on pre-trained language models. We compare and analyze the effectiveness and efficiency of using semantic matching modules with different setups on four types of MRC datasets. We verify that using semantic matching before a reading model improves both the effectiveness and efficiency of MRC. Compared with answering questions by extracting information from concise context, we observe that semantic matching yields more improvements for answering questions with noisy and adversarial context. Matching coarse-grained context to questions, e.g., paragraphs, is more effective than matching fine-grained context, e.g., sentences and spans. We also find that semantic matching is helpful for answering who/where/when/what/how/which questions, whereas it decreases the MRC performance on why questions. This may imply that semantic matching helps to answer a question whose necessary information can be retrieved from a single sentence. The above observations demonstrate the advantages and disadvantages of using semantic matching in different scenarios. 相似文献
17.
Antonio Jimeno-Yepes Rafael Berlanga-Llavori Dietrich Rebholz-Schuhmann 《Information processing & management》2010
Ontologies are frequently used in information retrieval being their main applications the expansion of queries, semantic indexing of documents and the organization of search results. Ontologies provide lexical items, allow conceptual normalization and provide different types of relations. However, the optimization of an ontology to perform information retrieval tasks is still unclear. In this paper, we use an ontology query model to analyze the usefulness of ontologies in effectively performing document searches. Moreover, we propose an algorithm to refine ontologies for information retrieval tasks with preliminary positive results. 相似文献
18.
Mining linkage information from the citation graph has been shown to be effective in identifying important literatures. However, the question of how to utilize linkage information from the citation graph to facilitate literature retrieval still remains largely unanswered. In this paper, given the context of biomedical literature retrieval, we first conduct a case study in order to find out whether applying PageRank and HITS algorithms directly to the citation graph is the best way of utilizing citation linkage information for improving biomedical literature retrieval. Second, we propose a probabilistic combination framework for integrating citation information into the content-based information retrieval weighting model. Based on the observations of the case study, we present two strategies for modeling the linkage information contained in the citation graph. The proposed framework provides a theoretical support for the combination of content and linkage information. Under this framework, exhaustive parameter tuning can be avoided. Extensive experiments on three TREC Genomics collections demonstrate the advantages and effectiveness of our proposed methods. 相似文献
19.
《Journal of The Franklin Institute》2023,360(6):4272-4296
High-resolution probabilistic load forecasting can comprehensively characterize both the uncertainties and the dynamic trends of the future load. Such information is key to the reliable operation of the future power grid with a high penetration of renewables. To this end, various high-resolution probabilistic load forecasting models have been proposed in recent decades. Compared with a single model, it is widely acknowledged that combining different models can further enhance the prediction performance, which is called the model ensemble. However, existing model ensemble approaches for load forecasting are linear combination-based, like mean value ensemble, weighted average ensemble, and quantile regression, and linear combinations may not fully utilize the advantages of different models, seriously limiting the performance of the model ensemble. We propose a learning ensemble approach that adopts the machine learning model to directly learn the optimal nonlinear combination from data. We theoretically demonstrate that the proposed learning ensemble approach can outperform conventional ensemble approaches. Based on the proposed learning ensemble model, we also introduce a Shapley value-based method to evaluate the contributions of each model to the model ensemble. The numerical studies on field load data verify the remarkable performance of our proposed approach. 相似文献
20.
《Information processing & management》2019,56(3):1026-1045
The study of query performance prediction (QPP) in information retrieval (IR) aims to predict retrieval effectiveness. The specificity of the underlying information need of a query often determines how effectively can a search engine retrieve relevant documents at top ranks. The presence of ambiguous terms makes a query less specific to the sought information need, which in turn may degrade IR effectiveness. In this paper, we propose a novel word embedding based pre-retrieval feature which measures the ambiguity of each query term by estimating how many ‘senses’ each word is associated with. Assuming each sense roughly corresponds to a Gaussian mixture component, our proposed generative model first estimates a Gaussian mixture model (GMM) from the word vectors that are most similar to the given query terms. We then use the posterior probabilities of generating the query terms themselves from this estimated GMM in order to quantify the ambiguity of the query. Previous studies have shown that post-retrieval QPP approaches often outperform pre-retrieval ones because they use additional information from the top ranked documents. To achieve the best of both worlds, we formalize a linear combination of our proposed GMM based pre-retrieval predictor with NQC, a state-of-the-art post-retrieval QPP. Our experiments on the TREC benchmark news and web collections demonstrate that our proposed hybrid QPP approach (in linear combination with NQC) significantly outperforms a range of other existing pre-retrieval approaches in combination with NQC used as baselines. 相似文献