scholarly journals Can MOOC Instructor Be Portrayed by Semantic Features? Using Discourse and Clustering Analysis to Identify Lecture-Style of Instructors in MOOCs

2021 ◽  
Vol 12 ◽  
Author(s):  
Changcheng Wu ◽  
Junyi Li ◽  
Ye Zhang ◽  
Chunmei Lan ◽  
Kaiji Zhou ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Analysis ◽  
Sentence Length ◽  
Semantic Features ◽  
Course Quality ◽  
Fine Grained ◽  
Massive Open Online Course ◽  
Complex Words

Nowadays, most courses in massive open online course (MOOC) platforms are xMOOCs, which are based on the traditional instruction-driven principle. Course lecture is still the key component of the course. Thus, analyzing lectures of the instructors of xMOOCs would be helpful to evaluate the course quality and provide feedback to instructors and researchers. The current study aimed to portray the lecture styles of instructors in MOOCs from the perspective of natural language processing. Specifically, 129 course transcripts were downloaded from two major MOOC platforms. Two semantic analysis tools (linguistic inquiry and word count and Coh-Metrix) were used to extract semantic features including self-reference, tone, effect, cognitive words, cohesion, complex words, and sentence length. On the basis of the comments of students, course video review, and the results of cluster analysis, we found four different lecture styles: “perfect,” “communicative,” “balanced,” and “serious.” Significant differences were found between the different lecture styles within different disciplines for notes taking, discussion posts, and overall course satisfaction. Future studies could use fine-grained log data to verify the results of our study and explore how to use the results of natural language processing to improve the lecture of instructors in both MOOCs and traditional classes.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

scholarly journals Predicting Verification Methods from Natural Language Requirements

2020 ◽  
Author(s):  
Michael Prendergast
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Analysis ◽  
Nearest Neighbor ◽  
Large Set ◽  
Technical Requirements ◽  
Verification Methods ◽  
Natural Language Requirements ◽  
Processing Algorithms

Abstract – A Verification Cross-Reference Matrix (VCRM) is a table that depicts the verification methods for requirements in a specification. Usually requirement labels are rows, available test methods are columns, and an “X” in a cell indicates usage of a verification method for that requirement. Verification methods include Demonstration, Inspection, Analysis and Test, and sometimes Certification, Similarity and/or Analogy. VCRMs enable acquirers and stakeholders to quickly understand how a product’s requirements will be tested.Maintaining consistency of very large VCRMs can be challenging, and inconsistent verification methods can result in a large set of uncoordinated “spaghetti tests”. Natural language processing algorithms that can identify similarities between requirements offer promise in addressing this challenge.This paper applies and compares compares four natural language processing algorithms to the problem of automatically populating VCRMs from natural language requirements: Naïve Bayesian inference, (b) Nearest Neighbor by weighted Dice similarity, (c) Nearest Neighbor with Latent Semantic Analysis similarity, and (d) an ensemble method combining the first three approaches. The VCRMs used for this study are for slot machine technical requirements derived from gaming regulations from the countries of Australia and New Zealand, the province of Nova Scotia (Canada), the state of Michigan (United States) and recommendations from the International Association of Gaming Regulators (IAGR).

A Comparative Study of Deep and Shallow Parsing Approaches to Automated Grammaticality Evaluation

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
MK Aregbesola ◽  
RA Ganiyu ◽  
SO Olabiyisi ◽  
EO Omidiora
Keyword(s):  
Natural Language Processing ◽  
Comparative Analysis ◽  
Natural Language ◽  
Comparative Study ◽  
Language Processing ◽  
Sentence Length ◽  
Phrase Structure Grammar ◽  
Lexical Frequency ◽  
Grammar Rules ◽  
Shallow Parsing

The concept of automated grammar evaluation of natural language texts is one that has attracted significant interests in the natural language processing community. It is the examination of natural language text for grammatical accuracy using computer software. The current work is a comparative study of different deep and shallow parsing techniques that have been applied to lexical analysis and grammaticality evaluation of natural language texts. The comparative analysis was based on data gathered from numerous related works. Shallow parsing using induced grammars was first examined along with its two main sub-categories, the probabilistic statistical parsers and the connectionist approach using neural networks. Deep parsing using handcrafted grammar was subsequently examined along with several of it‟s subcategories including Transformational Grammars, Feature Based Grammars, Lexical Functional Grammar (LFG), Definite Clause Grammar (DCG), Property Grammar (PG), Categorial Grammar (CG), Generalized Phrase Structure Grammar (GPSG), and Head-driven Phrase Structure Grammar (HPSG). Based on facts gathered from literature on the different aforementioned formalisms, a comparative analysis of the deep and shallow parsing techniques was performed. The comparative analysis showed among other things that while the shallow parsing approach was usually domain dependent, influenced by sentence length and lexical frequency and employed machine learning to induce grammar rules, the deep parsing approach were not domain dependent, not influenced by sentence length nor lexical frequency, and they made use of well spelt out set of precise linguistic rules. The deep parsing techniques proved to be a more labour intensive approach while the induced grammar rules were usually faster and reliability increased with size, accuracy and coverage of training data. The shallow parsing approach has gained immense popularity owing to availability of large corpora for different languages, and has therefore become the most accepted and adopted approach in recent times. Keywords: Grammaticality, Natural language processing, Deep parsing, Shallow parsing, Handcrafted grammar, Precision grammar, Induced grammar, Automated scoring, Computational linguistics, Comparative study.

scholarly journals Plagiarism Detection Technique using www and Wordnet

2021 ◽  
pp. 1-6
Author(s):  
Dr. Kamlesh Sharma ◽  
◽  
Nidhi Garg ◽  
Arun Pandey ◽  
Daksh Yadav ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Analysis ◽  
Detection Technique ◽  
Linguistic Variation ◽  
Plagiarism Detection ◽  
Detection Techniques ◽  
Day By Day

Plagiarism is an act of using another person’s words, idea or information without giving credit to that person and presenting them as your own. With the development of the technologies in recent years, the act of Plagiarism increases significantly. But luckily the plagiarism detection techniques are available and they are improving day by day to detect the attempts of plagiarizing the content in education. The software like Turnitin, iThenticate or Safe Assign is available in the markets that are doing a great job in this context. But the problem is not fully solved yet. These software(s) still doesn’t detect the rephrasing of statements of another writer in other words. This paper primarily focuses to detect the plagiarism in the suspicious document based on the meaning and linguistic variation of the content. The techniques used for this context is based on Natural language processing. In this Paper, we present how the semantic analysis and syntactic driven Parsing can be used to detect the plagiarism.

scholarly journals Development of Cross Language Clone Detector for C, C++ & Java Repositories using Natural Language Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 2289-2293
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Code Clones ◽  
Code Base ◽  
Value Decomposition ◽  
Cross Language ◽  
Bug Fixes

Reusing the code with or without modification is common process in building all the large codebases of system software like Linux, gcc , and jdk. This process is referred to as software cloning or forking. Developers always find difficulty of bug fixes in porting large code base from one language to other native language during software porting. There exist many approaches in identifying software clones of same language that may not contribute for the developers involved in porting hence there is a need for cross language clone detector. This paper uses primary Natural Language Processing (NLP) approach using latent semantic analysis to find the cross language clones of other neighboring languages in terms of all 4 types of clones using latent semantic analysis algorithm that uses Singular value decomposition. It takes input as code(C, C++ or Java) and matches all the neighboring code clones in the static repository in terms of frequency of lines matched

Semantic Analysis of Big Data in the Problem of Forecasting the Innovative Development of the Energy Infrastructure of the Russian Federation

2020 ◽  
Author(s):  
Alexey Kopaygorodsky ◽  
I. Khayrullin ◽  
E. Khayrullina
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Russian Federation ◽  
Semantic Analysis ◽  
Innovative Development ◽  
Energy Infrastructure ◽  
Automate Monitoring ◽  
The Russian Federation

The article discusses the use of methods of semantic analysis and natural language processing to support research and forecasting the innovative development of the energy infrastructure of the Russian Federation. The existing methods and approaches to the organization of monitoring of technological solutions and innovative scientific developments are considered. To automate monitoring, the authors propose the use of natural language processing (NLP) methods. Semantic analysis and knowledge integration are based on a system of ontologies. The paper presents the main methods and approaches to building an infrastructure for processing open Big Data. Application of the proposed methods makes it possible to improve the quality of scientific research in this area and make them better.

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