Phonetic based sentence level rewriting of questions typed by dyslexic spellers in an information retrieval context

2007 ◽  
Author(s):  
Laurianne Sitbon ◽  
Patrice Bellot ◽  
Philippe Blache
Keyword(s):  
Information Retrieval ◽  
Sentence Level

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

scholarly journals Fine-Grained Visual Textual Alignment for Cross-Modal Retrieval Using Transformer Encoders

2021 ◽  
Vol 17 (4) ◽  
pp. 1-23
Author(s):  
Nicola Messina ◽  
Giuseppe Amato ◽  
Andrea Esuli ◽  
Fabrizio Falchi ◽  
Claudio Gennaro ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Large Scale ◽  
State Of The Art ◽  
Retrieval Task ◽  
Fine Grained ◽  
Retrieval Systems ◽  
Sentence Retrieval ◽  
Novel Approach ◽  
Sentence Level ◽  
Sentence Matching

Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level. Specifically, we present a novel approach called Transformer Encoder Reasoning and Alignment Network (TERAN). TERAN enforces a fine-grained match between the underlying components of images and sentences (i.e., image regions and words, respectively) to preserve the informative richness of both modalities. TERAN obtains state-of-the-art results on the image retrieval task on both MS-COCO and Flickr30k datasets. Moreover, on MS-COCO, it also outperforms current approaches on the sentence retrieval task. Focusing on scalable cross-modal information retrieval, TERAN is designed to keep the visual and textual data pipelines well separated. Cross-attention links invalidate any chance to separately extract visual and textual features needed for the online search and the offline indexing steps in large-scale retrieval systems. In this respect, TERAN merges the information from the two domains only during the final alignment phase, immediately before the loss computation. We argue that the fine-grained alignments produced by TERAN pave the way toward the research for effective and efficient methods for large-scale cross-modal information retrieval. We compare the effectiveness of our approach against relevant state-of-the-art methods. On the MS-COCO 1K test set, we obtain an improvement of 5.7% and 3.5% respectively on the image and the sentence retrieval tasks on the Recall@1 metric. The code used for the experiments is publicly available on GitHub at https://github.com/mesnico/TERAN .

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.

Exploring the Potential Impact of Sentence-Level Comprehension and Sentence-Level Fluency on Deaf Students' Passage Comprehension

2020 ◽  
Vol 63 (7) ◽  
pp. 2281-2292
Author(s):  
Ying Zhao ◽  
Xinchun Wu ◽  
Hongjun Chen ◽  
Peng Sun ◽  
Ruibo Xie ◽  
...  
Keyword(s):  
Independent Predictor ◽  
Vocabulary Knowledge ◽  
Group Differences ◽  
Regression Analyses ◽  
Deaf Students ◽  
Mediating Role ◽  
Sentence Level ◽  
Mediating Mechanisms ◽  
Potential Impact ◽  
The Relationship

Purpose This exploratory study aimed to investigate the potential impact of sentence-level comprehension and sentence-level fluency on passage comprehension of deaf students in elementary school. Method A total of 159 deaf students, 65 students ( M age = 13.46 years) in Grades 3 and 4 and 94 students ( M age = 14.95 years) in Grades 5 and 6, were assessed for nonverbal intelligence, vocabulary knowledge, sentence-level comprehension, sentence-level fluency, and passage comprehension. Group differences were examined using t tests, whereas the predictive and mediating mechanisms were examined using regression modeling. Results The regression analyses showed that the effect of sentence-level comprehension on passage comprehension was not significant, whereas sentence-level fluency was an independent predictor in Grades 3–4. Sentence-level comprehension and fluency contributed significant variance to passage comprehension in Grades 5–6. Sentence-level fluency fully mediated the influence of sentence-level comprehension on passage comprehension in Grades 3–4, playing a partial mediating role in Grades 5–6. Conclusions The relative contributions of sentence-level comprehension and fluency to deaf students' passage comprehension varied, and sentence-level fluency mediated the relationship between sentence-level comprehension and passage comprehension.

Sign in / Sign up

Export Citation Format

Share Document