Automatic Classification of Error Types in Solutions to Programming Assignments at Online Learning Platform

Improving mention detection for Basque based on a deep error analysis

Natural Language Engineering ◽

10.1017/s1351324916000206 ◽

2016 ◽

Vol 23 (3) ◽

pp. 351-384 ◽

Cited By ~ 2

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.

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Course Classification of Online Learning Platform Based on Sentence-Bert Model

Communications in Computer and Information Science - Big Data and Security ◽

10.1007/978-981-16-3150-4_50 ◽

2021 ◽

pp. 612-623

Author(s):

Jiaze He ◽

Qian Lu ◽

Ying Tong ◽

Yiyang Chen

Keyword(s):

Online Learning ◽

Learning Platform

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Interpreting HVEM of muscle-cell impulse networks

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010012103x ◽

1992 ◽

Vol 50 (1) ◽

pp. 126-127

Author(s):

Paul DeCosta ◽

Kyugon Cho ◽

Stephen Shemlon ◽

Heesung Jun ◽

Stanley M. Dunn

Keyword(s):

Structural Analysis ◽

Muscle Cell ◽

Electron Micrographs ◽

Relative Size ◽

Automatic Classification ◽

Nerve Fibers ◽

Analysis Algorithm ◽

Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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Automatic classification of sleep stages

Electroencephalography and Clinical Neurophysiology ◽

10.1016/s0013-4694(97)88102-7 ◽

1997 ◽

Vol 103 (1) ◽

pp. 44 ◽

Cited By ~ 2

Author(s):

B Kemp

Keyword(s):

Automatic Classification ◽

Sleep Stages

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An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0107 ◽

2017 ◽

Vol 7 (7) ◽

pp. 408

Author(s):

Yashpal Jitarwal ◽

Tabrej Ahamad Khan ◽

Pawan Mangal

Keyword(s):

Image Processing ◽

Texture Features ◽

Automatic Classification ◽

Processing Technique ◽

Image Processing Technique ◽

Advance Technique ◽

Time Required ◽

Fruit Sorting ◽

Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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