Classification of Error-Correcting Coded Data Using Multidimensional Feature Vectors

2014 ◽  
pp. 303-312 ◽  
Author(s):  
Rajesh Asthana ◽  
Anand Sharma ◽  
Ram Ratan ◽  
Neelam Verma
Keyword(s):  
Feature Vectors ◽  
Classification Of Error

Classification of Error in Abdominal Imaging: Pearls and Pitfalls for Radiologists

2018 ◽  
Vol 69 (4) ◽  
pp. 409-416 ◽  
Author(s):  
Csilla Egri ◽  
Kathryn E. Darras ◽  
Elena P. Scali ◽  
Alison C. Harris
Keyword(s):  
Peer Review ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Abdominal Imaging ◽  
Patient Harm ◽  
Contributing Factors ◽  
Abdominal Radiology ◽  
Pictorial Review ◽  
Classification Of Error

Peer review for radiologists plays an important role in identifying contributing factors that can lead to diagnostic errors and patient harm. It is essential that all radiologists be aware of the multifactorial causes of diagnostic error in radiology and the methods available to reduce it. This pictorial review provides readers with an overview of common errors that occur in abdominal radiology and strategies to reduce them. This review aims to make readers more aware of pitfalls in abdominal imaging so that these errors can be avoided in the future. This essay also provides a systematic approach to classifying abdominal imaging errors that will be of value to all radiologists participating in peer review.

Classification of Error Correction Methods, a Broad View

2011 ◽  
pp. 85-94
Author(s):  
Georgi Radulov ◽  
Patrick Quinn ◽  
Hans Hegt ◽  
Arthur van Roermund
Keyword(s):  
Error Correction ◽  
Broad View ◽  
Classification Of Error

Taxonomy and Epidemiology of Error in Radiology

2019 ◽  
pp. 29-74
Author(s):  
Michael A. Bruno
Keyword(s):  
Working Memory ◽  
Cognitive Biases ◽  
Confirmation Bias ◽  
Diagnostic Error ◽  
Attentional Focus ◽  
Relative Contribution ◽  
Anchoring Bias ◽  
Classification Of Error

This chapter provides an overview of the prevalence and classification of error types in radiology, including the frequency and types of errors made by radiologists. We will review the relative contribution of perceptual error—in which findings are simply not seen—as compared to other common types of error. This error epidemiology will be considered in the light of the underlying variability and uncertainties present in the radiological process. The role of key cognitive biases will also be reviewed, including anchoring bias, confirmation bias, and availability bias. The role of attentional focus, working memory, and problems caused by fatigue and interruption will also be explored. Finally, the problem of radiologist error will be considered in the context of the overall problem of diagnostic error in medicine.

Improving mention detection for Basque based on a deep error analysis

2016 ◽  
Vol 23 (3) ◽  
pp. 351-384 ◽  
Author(s):  
ANDER SORALUZE ◽  
OLATZ ARREGI ◽  
XABIER ARREGI ◽  
ARANTZA DÍAZ DE ILARRAZA
Keyword(s):  
Error Analysis ◽  
Coreference Resolution ◽  
Linguistic Processing ◽  
Exact Matching ◽  
Standard Data ◽  
Percentage Points ◽  
Classification Of Error ◽  
F Measure ◽  
Error Types

AbstractThis paper presents the improvement process of a mention detector for Basque. The system is rule-based and takes into account the characteristics of mentions in Basque. A classification of error types is proposed based on the errors that occur during mention detection. A deep error analysis distinguishing error types and causes is presented and improvements are proposed. At the final stage, the system obtains an F-measure of 74.57% under the Exact Matching protocol and of 80.57% under Lenient Matching. We also show the performance of the mention detector with gold standard data as input, in order to omit errors caused by the previous stages of linguistic processing. In this scenario, we obtain an F-measure of 85.89% with Strict Matching and of 89.06% with Lenient Matching, i.e., a difference of 11.32 and 8.49 percentage points, respectively. Finally, how improvements in mention detection affect coreference resolution is analysed.

scholarly journals Classification of Microblog Users’ Sentiments Based on BERT-BiLSTM-CBAM

CONVERTER ◽  
2021 ◽  
pp. 281-287
Author(s):  
Juan Chen, Ruyun Chen, Di Yu
Keyword(s):  
Hybrid Model ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Feature Vectors ◽  
Text Information ◽  
Memory Network ◽  
Long Short Term Memory ◽  
New Feature

This study proposes a method for more accurate classification of microblog users' sentiments based on the BERT-BiLSTM-CBAM hybrid model. First, the text information is pre-trained by the bidirectional encoder representation from transformers (BERT) model to get feature vectors. Then, the feature vectors are spliced and recombined using bidirectional long-short-term memory network (BiLSTM) and CBAM mechanism to obtain new feature vectors. Finally, these new feature vectors are input to the full connection layer and then processed by the softmax function to obtain the sentiment category of the text. The experiment conducted on the sample dataset demonstrates that the model proposed in this study yielded accurate and dependable result in classifying microblog texts in the sample dataset. The model based on BERT-BiLSTM-CBAM algorithm is more efficient than the traditional depth model in processing microblog contents

scholarly journals A Corpus-Based Study of the Linguistic Errors Committed by the Iranian EFL Learners in English Translation of MA Research Abstracts of Educational Management Based on Liao’s (2010) Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bahar Pourshahian
Keyword(s):  
Quantitative Research ◽  
Target Language ◽  
Original Text ◽  
Educational Management ◽  
English Translations ◽  
Free Translation ◽  
Classification Of Error ◽  
Saturation Method ◽  
Ma Thesis

Given the importance and the precision required in the translation of research abstracts, this descriptive quantitative research made an attempt to investigate the analysis of the type and frequency of the linguistic errors occurring in the English translations of 40 academic MA research abstracts in the field of educational management. To this end, 40 academic MA thesis abstracts in the field of educational management from 2009 to 2019 were gathered from Shiraz Azad University through the saturation method. Then, the errors were categorized based on the classification of error types adapted from Liao’s model (2010). The results of the study revealed that based on Liao’s categorization (2010), the frequencies of possible linguistic errors by educational management include grammatical mistake or ungrammatical syntax of target language (F = 190), excessive literal translation, which leads to ambiguous translation (F = 30), awkward expression, including ambiguous meaning, mismatch, redundant words, and unnecessary repetition, (F = 29), incorrect character, improper punctuation marks, or inconsistency in term translation (F = 26), excessive free translation, which differentiates the translation from the original text (F = 6), and inappropriate register (F = 6).

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