Automated Scoring System Using Dependency-Based Weighted Semantic Similarity Model

2009 ◽  
Author(s):  
Liang Chen ◽  
Yajun Liu
Keyword(s):  
Semantic Similarity ◽  
Scoring System ◽  
Automated Scoring ◽  
Similarity Model

scholarly journals A Study of Applying Automated Assessment in Teaching College English Writing Based on Juku Correction Network

2019 ◽  
Vol 14 (11) ◽  
pp. 19
Author(s):  
Yusheng Wang
Keyword(s):  
Scoring System ◽  
Teaching Model ◽  
Network Technology ◽  
Automated Scoring ◽  
Teaching Mode ◽  
College English ◽  
English Writing ◽  
Writing Motivation ◽  
Teaching College ◽  
Writing Course

With the continuous advancement of modern network technology, the drawbacks of the tradition-al English writing course teaching mode have become increasingly prominent, and the automated scoring system has gradually been used in the writing course. This paper proposes a college English writing teaching model based on Juku Correction Network, and conducts empirical re-search on the use of Juku Correction Network in college English writing teaching. The research results show that the teaching mode based on Juku Correction Network can effectively improve the overall level of students' English writing, and stimulate students' English writing motivation.

Collective Entity Disambiguation Based on Hierarchical Semantic Similarity

2020 ◽  
Vol 16 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Bingjing Jia ◽  
Hu Yang ◽  
Bin Wu ◽  
Ying Xing
Keyword(s):  
Semantic Similarity ◽  
Semantic Matching ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Global Features ◽  
Global Coherence ◽  
Similarity Model ◽  
Benchmark Datasets ◽  
Entity Disambiguation ◽  
Multiple Granularities

Entity disambiguation involves mapping mentions in texts to the corresponding entities in a given knowledge base. Most previous approaches were based on handcrafted features and failed to capture semantic information over multiple granularities. For accurately disambiguating entities, various information aspects of mentions and entities should be used in. This article proposes a hierarchical semantic similarity model to find important clues related to mentions and entities based on multiple sources of information, such as contexts of the mentions, entity descriptions and categories. This model can effectively measure the semantic matching between mentions and target entities. Global features are also added, including prior popularity and global coherence, to improve the performance. In order to verify the effect of hierarchical semantic similarity model combined with global features, named HSSMGF, experiments were carried out on five publicly available benchmark datasets. Results demonstrate the proposed method is very effective in the case that documents have more mentions.

Automated scoring system of standard uptake value for torso FDG-PET images

2008 ◽  
Author(s):  
Takeshi Hara ◽  
Tatsunori Kobayashi ◽  
Kazunao Kawai ◽  
Xiangrong Zhou ◽  
Satoshi Itoh ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Scoring System ◽  
Standard Uptake Value ◽  
Automated Scoring
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