scholarly journals An Improved TextRank Multi-feature Fusion Algorithm For Keyword Extraction of Educational Resources

2021 ◽  
Vol 2078 (1) ◽  
pp. 012021
Author(s):  
Hongyang Zhao ◽  
Qiang Xie
Keyword(s):  
Chinese Text ◽  
Feature Fusion ◽  
Graph Model ◽  
Recall Rate ◽  
Educational Resources ◽  
Keyword Extraction ◽  
Semantic Features ◽  
Fusion Algorithm ◽  
General Semantic

Abstract In view of the fact that the traditional graph model method which only considers statistical features or general semantic features when extracting keywords from existing massive educational resources, lacks the function of mining and utilizing multi-factor semantic features, this paper proposes an improved TextRank-based algorithm for keyword extraction of educational resources. According to the characteristics of Chinese text and the shortcomings of traditional TextRank algorithm, the improved algorithm featuring multi-feature fusion is developed using the importance of words in the corpus, the location information in the text and the attributes of words. Experimental results show that this method has higher accuracy, recall rate, and F-measure value than traditional algorithms in the process of keyword extraction of educational resources, which improves the quality of keyword extraction and is beneficial to better utilization and management of educational resources.

scholarly journals Using the Ship-Gram Model for Japanese Keyword Extraction Based on News Reports

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Miao Teng
Keyword(s):  
Experimental Studies ◽  
Main Idea ◽  
Graph Model ◽  
Structural Features ◽  
Keyword Extraction ◽  
Semantic Features ◽  
News Reports ◽  
Semantic Coherence ◽  
Depth Study ◽  
Cosine Distance

In this paper, we conduct an in-depth study of Japanese keyword extraction from news reports, train external computer document word sets from text preprocessing into word vectors using the Ship-gram model in the deep learning tool Word2Vec, and calculate the cosine distance between word vectors. In this paper, the sliding window in TextRank is designed to connect internal document information to improve the in-text semantic coherence. The main idea is to use not only the statistical and structural features of words but also the semantic features of words extracted through word-embedding techniques, i.e., multifeature fusion, to obtain the importance weights of words themselves and the attraction weights between words and then iteratively calculate the final weight of each word through the graph model algorithm to determine the extracted keywords. To verify the performance of the algorithm, extensive simulation experimental studies were conducted on three different types of datasets. The experimental results show that the proposed keyword extraction algorithm can improve the performance by a maximum of 6.45% and 20.36% compared with the existing word frequency statistics and graph model methods, respectively; MF-Rank can achieve a maximum performance improvement of 1.76% compared with PW-TF.

scholarly journals Application of keyword extraction on MOOC resources

2017 ◽  
Vol 1 (1) ◽  
pp. 48-70
Author(s):  
Zhuoxuan Jiang ◽  
Chunyan Miao ◽  
Xiaoming Li
Keyword(s):  
Rapid Development ◽  
Graph Model ◽  
Real Data ◽  
Educational Resources ◽  
Keyword Extraction ◽  
Learning Resources ◽  
Teaching Resources ◽  
Content Type ◽  
Building Models ◽  
New Findings

Purpose Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well. Design/methodology/approach Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels. Findings From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform. Research limitations/implications Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required. Practical implications The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved. Originality/value This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

scholarly journals Deep Superpixel-Based Network For Blind Image Quality Assessment

2021 ◽  
Author(s):  
Guangyi Yang ◽  
Yang Zhan ◽  
Yuxuan Wang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Feature Fusion ◽  
Assessment Process ◽  
Semantic Features ◽  
Multi Scale ◽  
Blind Image Quality Assessment ◽  
Blind Image

Abstract The goal in a blind image quality assessment (BIQA) method is to simulate the process of evaluating images by human eyes and accurately assess the quality of the image. Although many methods effectively identify degradation, they do not fully consider the semantic content in images resulting in distortion. In order to fill this gap, we propose a deep adaptive superpixel-based network, namely DSN-IQA, to assess the quality of image based on multi-scale and superpixel segmentation. The DSN-IQA can adaptively accept arbitrary scale images as input images, making the assessment process similar to human perception. The network uses two models to extract multi-scale semantic features and generate a superpixel adjacency map. These two elements are united together via feature fusion to accurately predict image quality. Experimental results on different benchmark databases demonstrate that our method is highly competitive with other methods when assessing challenging authentic image databases. Also, due to adaptive deep superpixel-based network, our method accurately assesses images with complicated distortion, much like the human eye.

scholarly journals Multideep Feature Fusion Algorithm for Clothing Style Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yuhua Li ◽  
Zhiqiang He ◽  
Sunan Wang ◽  
Zicheng Wang ◽  
Wanwei Huang
Keyword(s):  
Feature Extraction ◽  
Target Detection ◽  
Main Part ◽  
Feature Fusion ◽  
Extraction Procedure ◽  
Semantic Features ◽  
Residual Network ◽  
Fusion Algorithm ◽  
Detection Model ◽  
Clothing Style

In order to improve recognition accuracy of clothing style and fully exploit the advantages of deep learning in extracting deep semantic features from global to local features of clothing images, this paper utilizes the target detection technology and deep residual network (ResNet) to extract comprehensive clothing features, which aims at focusing on clothing itself in the process of feature extraction procedure. Based on that, we propose a multideep feature fusion algorithm for clothing image style recognition. First, we use the improved target detection model to extract the global area, main part, and part areas of clothing, which constitute the image, so as to weaken the influence of the background and other interference factors. Then, the three parts were inputted, respectively, to improve ResNet for feature extraction, which has been trained beforehand. The ResNet model is improved by optimizing the convolution layer in the residual block and adjusting the order of the batch-normalized layer and the activation layer. Finally, the multicategory fusion features were obtained by combining the overall features of the clothing image from the global area, the main part, to the part areas. The experimental results show that the proposed algorithm eliminates the influence of interference factors, makes the recognition process focus on clothing itself, greatly improves the accuracy of the clothing style recognition, and is better than the traditional deep residual network-based methods.

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

EARLY PREDICTION OF MEDICATION REFRACTORINESS IN CHILDREN WITH IDIOPATHIC EPILEPSY BASED ON SCALP EEG ANALYSIS

2014 ◽  
Vol 24 (07) ◽  
pp. 1450023 ◽  
Author(s):  
LUNG-CHANG LIN ◽  
CHEN-SEN OUYANG ◽  
CHING-TAI CHIANG ◽  
REI-CHENG YANG ◽  
RONG-CHING WU ◽  
...  
Keyword(s):  
Refractory Epilepsy ◽  
Recall Rate ◽  
Idiopathic Epilepsy ◽  
Spectral Edge Frequency ◽  
Scalp Eeg ◽  
Feature Descriptors ◽  
Antiepileptic Drug Treatment ◽  
Eeg Recordings ◽  
Precision Rate

Refractory epilepsy often has deleterious effects on an individual's health and quality of life. Early identification of patients whose seizures are refractory to antiepileptic drugs is important in considering the use of alternative treatments. Although idiopathic epilepsy is regarded as having a significantly lower risk factor of developing refractory epilepsy, still a subset of patients with idiopathic epilepsy might be refractory to medical treatment. In this study, we developed an effective method to predict the refractoriness of idiopathic epilepsy. Sixteen EEG segments from 12 well-controlled patients and 14 EEG segments from 11 refractory patients were analyzed at the time of first EEG recordings before antiepileptic drug treatment. Ten crucial EEG feature descriptors were selected for classification. Three of 10 were related to decorrelation time, and four of 10 were related to relative power of delta/gamma. There were significantly higher values in these seven feature descriptors in the well-controlled group as compared to the refractory group. On the contrary, the remaining three feature descriptors related to spectral edge frequency, kurtosis, and energy of wavelet coefficients demonstrated significantly lower values in the well-controlled group as compared to the refractory group. The analyses yielded a weighted precision rate of 94.2%, and a 93.3% recall rate. Therefore, the developed method is a useful tool in identifying the possibility of developing refractory epilepsy in patients with idiopathic epilepsy.

STRUCTURAL AND SEMANTIC CHARACTERISTICS OF THE NAMES AND DESCRIPTIONS OF DISHES IN GERMAN RESTAURANT DISCOURSE

2021 ◽  
Vol 7 (2) ◽  
pp. 96-119
Author(s):  
Nadezhda G. KANTYSHEVA ◽  
Inna V. Solovyova
Keyword(s):  
Discourse Analysis ◽  
Semantic Features ◽  
Institutional Discourse ◽  
The World ◽  
Different Types ◽  
Complex Words ◽  
First Time ◽  
Comprehensive Study

This article is devoted to a comprehensive study of the structural and semantic features of dish names and their descriptions in German in the field of restaurant discourse. The study employs cognitive discourse analysis, elements of comparative and contextological approaches, taking into account linguocultural parameters. The relevance of the comprehensive study of the names of dishes in restaurant discourse is due to an increased interest in the parameterization of lexical units in different types of institutional discourse. The scientific novelty of this work lies in the fact that for the first time, within the framework of a restaurant menu, not only the nomination of a dish is considered, but also the structural and semantic characteristics of its description are analysed. An attempt is made to analyse a connection between the nominations of dishes and their description in the restaurant menu, as well as to determine the semantic dominants of the genre under study. It is concluded that the text of the menu as a whole presents a combination of the language for special purposes and the language of advertising. In interaction with extralinguistic factors, the nominations of dishes and their descriptions not only document the culture of food in society, but also reflect the ethnocultural picture of the world. Based on the analysis of the menu texts, it is established that structurally the names of dishes are complex words or phrases, built mainly according to the attributive model. The description of dishes performs the function of verbalizing the sensations of taste and clarifying the method of preparing dishes, characterizing the quality of dishes, their ingredients, and the intensity of taste. Evaluative parameters in descriptions are expressed at the lexical, grammatical, syntactic and stylistic levels.

scholarly journals Educational reform in Morocco in the light of TIMSS evaluation

2013 ◽  
Vol 4 (5) ◽  
Author(s):  
Ahmed AGHBAL
Keyword(s):  
Academic Performance ◽  
Educational Reform ◽  
Educational Resources ◽  
Family Context ◽  
Main Question ◽  
Contextual Variables ◽  
Cultural Variables ◽  
The Family ◽  
The Individual

The purpose of this study is to analyze Moroccan TIMSS 2011 data using the multi-level approach in order to detect the entanglement of factors that influence the academic performance of students in the second year of college secondary education (grade eight ). The main question is, what is the influence of factors related to the individual characteristics and characteristics of the school attended on the performance of students in mathematics? This question can be answered in many ways: 1. Does student achievement in mathematics vary by school? 2. Is there a significant relationship between the socioeconomic and cultural characteristics of the students and their performance in mathematics? the main question is whether the effect of socio-cultural variables linked to the family context (in particular family educational resources) tends to worsen when they interact with the contextual variables of the school. This raises the question of both the efficiency of schools and the equitable distribution of performance in mathematics. The question can be formulated in the following way: Does the effect of socio-cultural variables at the individual level increase with contact with socioeconomic variables at school level? 3. To what extent do the contextual variables of the school (school climate, educational resources available to schools, etc.) have a moderating or amplifying effect on the relationship between the socio-cultural variables linked to the family context of students and academic performance in math? 4. And what are the determinants of the quality of academic performance defined according to TIMSS standards? Added to this is the question of what has been the impact of the educational reform on the dynamics of school organizations and on the quality of academic performance.

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