scholarly journals Towards Question-based High-recall Information Retrieval

2020 ◽  
Vol 38 (3) ◽  
pp. 1-35 ◽  
Author(s):  
Jie Zou ◽  
Evangelos Kanoulas
Keyword(s):  
Information Retrieval ◽  
High Recall

scholarly journals Evaluating sentence-level relevance feedback for high-recall information retrieval

2019 ◽  
Vol 23 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Haotian Zhang ◽  
Gordon V. Cormack ◽  
Maura R. Grossman ◽  
Mark D. Smucker
Keyword(s):  
Information Retrieval ◽  
Relevance Feedback ◽  
High Recall ◽  
Sentence Level

Research on Multi-Agent Intelligent Information Retrieval Model

2015 ◽  
Vol 710 ◽  
pp. 139-143
Author(s):  
Rui Dai ◽  
Xiang Xu ◽  
Chang Feng Shi ◽  
Yi Lu
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
High Precision ◽  
Retrieval Model ◽  
High Recall ◽  
Model Based ◽  
Intelligent Information Retrieval ◽  
Multi Agent ◽  
Target Source ◽  
Intelligent Information

The intelligent information retrieval model based on multi-agent imitates and synthesizes the features of search engine and information retrieval in the website. It realizes high recall-precision and high precision of the retrieval effectively, and has good flexibility and expansibility. It is convenient for users to configure and choose target source of retrieval.

scholarly journals Comprehensive Review on Effectual Information Retrieval of Semantic Drift using Deep Neural Network

2019 ◽  
Vol 8 (1) ◽  
pp. 32-35
Author(s):  
A. Uma Maheswari ◽  
N. Revathy
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Information Extraction ◽  
Deep Neural Network ◽  
Research Work ◽  
Single Class ◽  
High Recall ◽  
Comprehensive Review ◽  
Substantial Loss ◽  
Semantic Lexicons

Semantic drift is a common problem in iterative information extraction. Unsupervised bagging and incorporated distributional similarity is used to reduce the difficulty of semantic drift in iterative bootstrapping algorithms, particularly when extracting large semantic lexicons. In this research work, a method to minimize semantic drift by identifying the (Drifting Points) DPs and removing the effect introduced by the DPs is proposed. Previous methods for identifying drifting errors can be roughly divided into two categories: (1) multi-class based, and (2) single-class based, according to the settings of Information Extraction systems that adopt them. Compared to previous approaches which usually incur substantial loss in recall, DP-based cleaning method can effectively clean a large proportion of semantic drift errors while keeping a high recall.

Active High-Recall Information Retrieval from Domain-Specific Text Corpora based on Query Documents

2018 ◽  
Author(s):  
Sitong Chen ◽  
Abidalrahman Moh'd ◽  
Seyednaser Nourashrafeddin ◽  
Evangelos Milios
Keyword(s):  
Information Retrieval ◽  
High Recall ◽  
Domain Specific ◽  
Text Corpora

High-Recall Information Retrieval from Linked Big Data

2015 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Wookey Lee ◽  
Carson K. Leung
Keyword(s):  
Big Data ◽  
Information Retrieval ◽  
High Recall

Demonstration of the Use of Tape-Recorded Information from a High Vacuum Electron Microscope

1969 ◽  
Vol 27 ◽  
pp. 218-219
Author(s):  
Richard E. Hartman ◽  
Roberta S. Hartman ◽  
Peter L. Ramos
Keyword(s):  
Information Retrieval ◽  
Electron Microscope ◽  
Magnetic Tape ◽  
High Vacuum ◽  
Data Retrieval ◽  
Electronic Information ◽  
Electronic Data ◽  
Subsequent Processing ◽  
Continuous Record

We have long felt that some form of electronic information retrieval would be more desirable than conventional photographic methods in a high vacuum electron microscope for various reasons. The most obvious of these is the fact that with electronic data retrieval the major source of gas load is removed from the instrument. An equally important reason is that if any subsequent analysis of the data is to be made, a continuous record on magnetic tape gives a much larger quantity of data and gives it in a form far more satisfactory for subsequent processing.

Stain contamination and embedding in electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 50-53
Author(s):  
Hilton H. Mollenhauer
Keyword(s):  
Electron Microscopy ◽  
Information Retrieval ◽  
Epoxy Resins ◽  
Image Contrast ◽  
Sample Preservation ◽  
Factors Associated ◽  
Amount Of Information ◽  
Electron Microscopical ◽  
Contrast Information

Many factors (e.g., resolution of microscope, type of tissue, and preparation of sample) affect electron microscopical images and alter the amount of information that can be retrieved from a specimen. Of interest in this report are those factors associated with the evaluation of epoxy embedded tissues. In this context, informational retrieval is dependant, in part, on the ability to “see” sample detail (e.g., contrast) and, in part, on tue quality of sample preservation. Two aspects of this problem will be discussed: 1) epoxy resins and their effect on image contrast, information retrieval, and sample preservation; and 2) the interaction between some stains commonly used for enhancing contrast and information retrieval.

Information and Dose in the Scanning Transmission Ion Microscope

1980 ◽  
Vol 38 ◽  
pp. 232-233
Author(s):  
Fox T. R. ◽  
R. Levi-Setti
Keyword(s):  
Information Retrieval ◽  
Electron Microscope ◽  
Elastic Scattering ◽  
Energy Loss ◽  
Cross Sections ◽  
Previous Analysis ◽  
Single Scattering ◽  
Screening Length ◽  
Relative Damage ◽  
Scanning Transmission

At an earlier meeting [1], we discussed information retrieval in the scanning transmission ion microscope (STIM) compared with the electron microscope at the same energy. We treated elastic scattering contrast, using total elastic cross sections; relative damage was estimated from energy loss data. This treatment is valid for “thin” specimens, where the incident particles suffer only single scattering. Since proton cross sections exceed electron cross sections, a given specimen (e.g., 1 μg/cm2 of carbon at 25 keV) may be thin for electrons but “thick” for protons. Therefore, we now extend our previous analysis to include multiple scattering. Our proton results are based on the calculations of Sigmund and Winterbon [2], for 25 keV protons on carbon, using a Thomas-Fermi screened potential with a screening length of 0.0226 nm. The electron results are from Crewe and Groves [3] at 30 keV.

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