Use of Syntactic Similarity Based Similarity Matrix for Evaluating Descriptive Answer

2014 ◽  
Author(s):  
Dimple V. Paul ◽  
Jyoti D. Pawar
Keyword(s):  
Similarity Matrix ◽  
Syntactic Similarity

Answer Evaluation of Short Descriptive Questions

2021 ◽  
pp. 104-118
Keyword(s):  
Similarity Measure ◽  
Assessment System ◽  
Similarity Matrix ◽  
Test Paper ◽  
Continuous Assessment ◽  
Simple Interpretation ◽  
Cognitive Levels ◽  
Stages Of Learning ◽  
Syntactic Similarity ◽  
Measure Word

Reforms in the educational system emphasize more on continuous assessment. The descriptive examination test paper when compared to objective test paper acts as a better aid in continuous assessment for testing the progress of a student under various cognitive levels at different stages of learning. Unfortunately, assessment of descriptive answers is found to be tedious and time-consuming by instructors due to the increase in number of examinations in continuous assessment system. In this chapter, an attempt has been made to address the problem of automatic evaluation of descriptive answer using vector-based similarity matrix with order-based word-to-word syntactic similarity measure. Word order similarity measure remains as one of the best measures to find the similarity between sequential words in sentences and is increasing its popularity due to its simple interpretation and easy computation.

scholarly journals Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 403
Author(s):  
Xun Zhang ◽  
Lanyan Yang ◽  
Bin Zhang ◽  
Ying Liu ◽  
Dong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Structured Data ◽  
Similarity Matrix ◽  
Scale Feature ◽  
Multi Scale ◽  
Adjacent Node ◽  
Feature Similarity ◽  
The Mean ◽  
Graph Neural Networks

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.

Ampelographic characterization of white grapevine cul-tivars (Vitis vinifera L.) grown in Palestine

2015 ◽  
Vol 3 (1) ◽  
pp. 1-11
Author(s):  
Rezq Basheer-Salimia
Keyword(s):  
Genetic Diversity ◽  
Genetic Distance ◽  
Germplasm Conservation ◽  
Genetic Distances ◽  
Vitis Vinifera L ◽  
Similarity Matrix ◽  
Breeding Programs ◽  
Local Varieties ◽  
Synonymies And Homonymies

Abstract: In Palestine, grape culture consists of ecotypes and cultivars (also called local varieties), for which a large number of homonymous and synonymous designations exist as well as misnaming of cultivars. The present study is the first report using detailed ampelographic characterizations (39 informative traits) to assess genetic diversity and detect similarities among sixteen accessions collected from putative diverse grape genotypes In general, 30 descriptors presented highly and satisfactory divergent genotypes, whereas the remaining traits showed no or very little ampelographic variation. Based on the similarity matrix and the resulting dendrogram of these ampelographic data, distinguishable genotypes as well as some cases of synonymies and homonymies clearly exist. A synonymy case seemed to be in four genotypes including Jandali-Mfarad, Jan-dali-Mrazraz, Jandali, and Hamadani-Mattar, which indeed showed genetic distances of less than 0.5, sug-gesting their relatedness, and the possibility that they are the same genotype, but with different names. In addition, homonym cases also occur in the following pairs of “Marawi’s, Hamadani’s, and Zaini’s genotypes, in which each pair seems to be two distinctive genotypes. Finally, among the 16 examined genotypes, the Zaini-Baladi genotype tended to show the highest genetic distance values from the others and thus could be potentially incorporated into any further local or regional breeding programs as well as germplasm conservation.

Multiview Common Subspace Clustering via Coupled Low Rank Representation

2021 ◽  
Vol 12 (4) ◽  
pp. 1-25
Author(s):  
Stanley Ebhohimhen Abhadiomhen ◽  
Zhiyang Wang ◽  
Xiangjun Shen ◽  
Jianping Fan
Keyword(s):  
Common Knowledge ◽  
Subspace Clustering ◽  
Laplacian Matrix ◽  
Low Rank ◽  
Connected Components ◽  
Similarity Matrix ◽  
Benchmark Datasets ◽  
Low Rank Representation ◽  
Low Dimensional ◽  
Shared Structure

Multi-view subspace clustering (MVSC) finds a shared structure in latent low-dimensional subspaces of multi-view data to enhance clustering performance. Nonetheless, we observe that most existing MVSC methods neglect the diversity in multi-view data by considering only the common knowledge to find a shared structure either directly or by merging different similarity matrices learned for each view. In the presence of noise, this predefined shared structure becomes a biased representation of the different views. Thus, in this article, we propose a MVSC method based on coupled low-rank representation to address the above limitation. Our method first obtains a low-rank representation for each view, constrained to be a linear combination of the view-specific representation and the shared representation by simultaneously encouraging the sparsity of view-specific one. Then, it uses the k -block diagonal regularizer to learn a manifold recovery matrix for each view through respective low-rank matrices to recover more manifold structures from them. In this way, the proposed method can find an ideal similarity matrix by approximating clustering projection matrices obtained from the recovery structures. Hence, this similarity matrix denotes our clustering structure with exactly k connected components by applying a rank constraint on the similarity matrix’s relaxed Laplacian matrix to avoid spectral post-processing of the low-dimensional embedding matrix. The core of our idea is such that we introduce dynamic approximation into the low-rank representation to allow the clustering structure and the shared representation to guide each other to learn cleaner low-rank matrices that would lead to a better clustering structure. Therefore, our approach is notably different from existing methods in which the local manifold structure of data is captured in advance. Extensive experiments on six benchmark datasets show that our method outperforms 10 similar state-of-the-art compared methods in six evaluation metrics.

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