Knowledge Representation for an Automated Normalized Answers Assessment System, Based on Text Comprehension Theories

Blitsas, Panagiotis; Grigoriadou, Maria

doi:10.1109/icalt.2008.106

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Feedback This study is intended to decrease learners' plagiaristic behavior in online assignments by providing automated feedback based on text mining analysis. (Blitsas & Grigoriadou, 2008) Online assignments…”

Section: Sentence Similaritymentioning

confidence: 99%

Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

148

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The explosive growth of online education environments is generating a massive volume of data, specially in text format from forums, chats, social networks, assessments, essays, among others. It produces exciting challenges on how to mine text data in order to find useful knowledge for educational stakeholders. Despite the increasing number of educational applications of text mining published recently, we have not found any paper surveying them. In this line, this work presents a systematic overview of the current status of the Educational Text Mining field. Our final goal is to answer three main research questions: Which are the text mining techniques most used in educational environments? Which are the most used educational resources? And which are the main applications or educational goals? Finally, we outline the conclusions and the more interesting future trends. This article is categorized under: Application Areas > Education and Learning Ensemble Methods > Text Mining

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Section: Sentence Similaritymentioning

confidence: 99%

Text mining in education

Mello

Andre

Pinheiro

et al. 2019

WIREs Data Min & Knowl

148

View full text Add to dashboard Cite

show abstract

“…The main aim of different natural language processing (NLP) methods is to achieve a human-like understanding of the text data. A successful such analytics can help to analyze large volume of unstructured and poor-quality data to discover the appropriate insights and hidden information [2]. Over the past years, text data has been used in numerous applications like Email Filtering, Documents organization, Sentiment Analysis, Opinion and Content mining, Speech Detection and many more.…”

Section: Introductionmentioning

confidence: 99%

Impact of Text Representation Techniques on Clustering Models

Ashwini

Shantala

Jan

2022

Preprint

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In today’s environment, an enormous amount of unstructured data is generated in an exponential manner. Understanding such complex unstructured data is imperative in the applications including analysis of social media data, image and video data, sensor data, medical data, and customer review data. Generally, clustering is a well-accepted model in classifying and analyzing such documents. An effective and efficient text clustering technique can significantly improve the task of document analysis and grouping with minimum human intervention. The main two factors in text clustering are the text representation model and the clustering algorithm. Firstly, unstructured data must be represented in a structured format for the analysis. Text representation models transform a large volume of text into vector representations by capturing the semantic information. In this paper, we are investigating the text document representation techniques, their limitations, and how text clustering is different from humans understanding of the text data. In this paper, we are aiming to focus on different text representation methods and clustering algorithms to demonstrate how the choice of representation techniques can impact on clustering results.

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Knowledge Representation for an Automated Normalized Answers Assessment System, Based on Text Comprehension Theories

Cited by 2 publications

References 4 publications

Text mining in education

Text mining in education

Impact of Text Representation Techniques on Clustering Models

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