Search Results for « Retrieval »

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

An evaluation of a large, operational full-text document-retrieval system (containing roughly 350,000 pages of text) shows the system to be retrieving less than 20 percent of the documents relevant to a particular search. The findings are discussed in terms of the theory and practice of full-text document retrieval.

Human action control relies on representations that integrate perception and action, but the relevant research is scattered over various experimental paradigms and the theorizing is overly paradigm-specific. To overcome this obstacle we propose BRAC (binding and retrieval in action control), an overarching, integrative framework that accounts for a wide range of seemingly unrelated findings by assuming 'two core processes: feature binding and retrieval'. In contrast to previous approaches, we define binding and retrieval as functionally different and separable processes that independently contribute to the observed effects. Furthermore, both processes are independently modulated by top-down and/or bottom-up processes. BRAC organizes the literature on action control in novel ways, and relates diverse independently investigated action-related phenomena from different research fields to each other.

Evaluating retrieval-augmented generation (RAG) presents challenges, particularly for retrieval models within these systems. Traditional end-to-end evaluation methods are computationally expensive. Furthermore, evaluation of the retrieval model's performance based on query-document relevance labels shows a small correlation with the RAG system's downstream performance. We propose a novel evaluation approach, eRAG, where each document in the retrieval list is individually utilized by the large language model within the RAG system. The output generated for each document is then evaluated based on the downstream task ground truth labels. In this manner, the downstream performance for each document serves as its relevance label. We employ various downstream task metrics to obtain document-level annotations and aggregate them using set-based or ranking metrics. Extensive experiments on a wide range of datasets demonstrate that eRAG achieves a higher correlation with downstream RAG performance compared to baseline methods, with improvements in Kendall's tau correlation ranging from 0.168 to 0.494. Additionally, eRAG offers significant computational advantages, improving runtime and consuming up to 50 times less GPU memory than end-to-end evaluation.

ND BUSINESSMAN CALVIN MOORES COINED the term information retrieval [10] to describe the process through which a prospective user of information can convert a request for information into a useful collection of references. "Information retrieval," he wrote, "embraces the intellectual aspects of the description of information and its specification for search, and also whatever systems, techniques, or machines that are employed Amarnath Gupta and Ramesh Jain 72 May 1997/Vol. 40, No. 5 COMMUNICATIONS OF THE ACM lar expressions to describe a clip. There is also a deeper reason: The information sought is inherently in the form of imagery that a textual language, however powerful, is unable to express adequately, making query processing inefficient. HE ROLE OF THE EMERGING FIELD OF visual information retrieval (VIR) systems is to go beyond text-based descri

In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. We present data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. Although numerical figures vary, overall trends cited by the sources are consistent and point to exponential growth in the past and in the coming decade. Hence it is not surprising that about 85% of Internet users surveyed claim using search engines and search services to find specific information. The same surveys show, however, that users are not satisfied with the performance of the current generation of search engines; the slow retrieval speed, communication delays, and poor quality of retrieved results (e.g., noise and broken links) are commonly cited problems. We discuss the development of new techniques targeted to resolve some of the problems associated with Web-based information retrieval and speculate on future trends.

This paper proposes the SVDNet for retrieval problems, with focus on the application of person re-identification (reID). We view each weight vector within a fully connected (FC) layer in a convolutional neuron network (CNN) as a projection basis. It is observed that the weight vectors are usually highly correlated. This problem leads to correlations among entries of the FC descriptor, and compromises the retrieval performance based on the Euclidean distance. To address the problem, this paper proposes to optimize the deep representation learning process with Singular Vector Decomposition (SVD). Specifically, with the restraint and relaxation iteration (RRI) training scheme, we are able to iteratively integrate the orthogonality constraint in CNN training, yielding the so-called SVDNet. We conduct experiments on the Market-1501, CUHK03, and DukeMTMC-reID datasets, and show that RRI effectively reduces the correlation among the projection vectors, produces more discriminative FC descriptors, and significantly improves the re-ID accuracy. On the Market-1501 dataset, for instance, rank-1 accuracy is improved from 55.3% to 80.5% for CaffeNet, and from 73.8% to 82.3% for ResNet-50.

Long-term memories are influenced by the emotion experienced during learning as well as by the emotion experienced during memory retrieval. The present article reviews the literature addressing the effects of emotion on retrieval, focusing on the cognitive and neurological mechanisms that have been revealed. The reviewed research suggests that the amygdala, in combination with the hippocampus and prefrontal cortex, plays an important role in the retrieval of memories for emotional events. The neural regions necessary for online emotional processing also influence emotional memory retrieval, perhaps through the reexperience of emotion during the retrieval process.