State of the Art in Information Extraction and Quantitative Analysis for Multimodality Biomolecular Imaging

2008 ◽  
Vol 96 (3) ◽  
pp. 512-531 ◽  
Author(s):  
W.M. Ahmed ◽  
S.J. Leavesley ◽  
B. Rajwa ◽  
M.N. Ayyaz ◽  
A. Ghafoor ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Information Extraction ◽  
State Of The Art ◽  
Biomolecular Imaging

scholarly journals Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Clément Dalloux ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Lucas Emanuel Silva Oliveira ◽  
Claudia Maria Cabral Moro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
State Of The Art ◽  
Automatic Detection ◽  
Brazilian Portuguese ◽  
Supervised Machine Learning ◽  
Biomedical Domain ◽  
Learning Approaches ◽  
Cross Domain ◽  
Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

scholarly journals What Can Be Accomplished with the State of the Art in Information Extraction? A Personal View

2018 ◽  
Vol 44 (4) ◽  
pp. 651-658
Author(s):  
Ralph Weischedel ◽  
Elizabeth Boschee
Keyword(s):  
Information Extraction ◽  
State Of The Art ◽  
The State ◽  
Personal View ◽  
Common Features ◽  
Research Challenges ◽  
The Common

Though information extraction (IE) research has more than a 25-year history, F1 scores remain low. Thus, one could question continued investment in IE research. In this article, we present three applications where information extraction of entities, relations, and/or events has been used, and note the common features that seem to have led to success. We also identify key research challenges whose solution seems essential for broader successes. Because a few practical deployments already exist and because breakthroughs on particular challenges would greatly broaden the technology’s deployment, further R&D investments are justified.

Generate and Test for Formulated Product Variants With Information Extraction and an In-Silico Model

2020 ◽  
pp. 223-250
Author(s):  
Sagar Sunkle ◽  
Deepak Jain ◽  
Krati Saxena ◽  
Ashwini Patil ◽  
Rinu Chacko ◽  
...  
Keyword(s):  
Information Extraction ◽  
Product Design ◽  
Chemical Industry ◽  
In Silico ◽  
State Of The Art ◽  
Graph Database ◽  
Extended Time ◽  
Time To Market ◽  
In Silico Model ◽  
Chemical Products

The chemical industry is expanding its focus from process-centered products to product-centered products. Of these, consumer chemical products and other similar formulated products are especially ubiquitous. State of the art in the formulated product design relies heavily on experts and their expertise, leading to extended time to market and increased costs. The authors show that it is possible to construct a graph database of various details of products from textual sources, both offline and online. Similar to the “generate and test” approach, they propose that it is possible to generate feasible design variants of a given type of formulated product using the database so constructed. If they restrict the set of products that are applied to the skin, they propose to test the generated design variants using an in-silico model. Even though this chapter is an account of the work in progress, the authors believe the gains they can obtain from a readily accessible database and its integration with an in-silico model are substantial.

Current and future methodologies for quantitative analysis of information transfer in sign language and gesture data

2017 ◽  
Vol 40 ◽  
Author(s):  
Evie Malaia
Keyword(s):  
Quantitative Analysis ◽  
Data Collection ◽  
Sign Language ◽  
Motion Capture ◽  
Information Transfer ◽  
State Of The Art ◽  
Video Data ◽  
Noninvasive Methods ◽  
Video Recordings ◽  
Biological Differentiation

AbstractState-of-the-art methods of analysis of video data now include motion capture and optical flow from video recordings. These techniques allow for biological differentiation between visual communication and noncommunicative motion, enabling further inquiry into neural bases of communication. The requirements for additional noninvasive methods of data collection and automatic analysis of natural gesture and sign language are discussed.

scholarly journals Span Model for Open Information Extraction on Accurate Corpus

2020 ◽  
Vol 34 (05) ◽  
pp. 9523-9530
Author(s):  
Junlang Zhan ◽  
Hai Zhao
Keyword(s):  
Information Extraction ◽  
State Of The Art ◽  
Training Dataset ◽  
Model Design ◽  
Test Set ◽  
Benchmark Test ◽  
Open Information Extraction ◽  
Test Sets ◽  
Improved Model ◽  
Benchmark Evaluation

Open Information Extraction (Open IE) is a challenging task especially due to its brittle data basis. Most of Open IE systems have to be trained on automatically built corpus and evaluated on inaccurate test set. In this work, we first alleviate this difficulty from both sides of training and test sets. For the former, we propose an improved model design to more sufficiently exploit training dataset. For the latter, we present our accurately re-annotated benchmark test set (Re-OIE2016) according to a series of linguistic observation and analysis. Then, we introduce a span model instead of previous adopted sequence labeling formulization for n-ary Open IE. Our newly introduced model achieves new state-of-the-art performance on both benchmark evaluation datasets.

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