NATURAL LANGUAGE PROCESSING FOR DATA MINING IN COMPUTER SCIENCE EDUCATION

2020 ◽  
Author(s):  
Fernando López-Ostenero ◽  
Laura Plaza ◽  
Juan Martinez-Romo ◽  
Lourdes Araujo
Keyword(s):  
Data Mining ◽  
Science Education ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
Computer Science Education

Using NLP for Fact Checking: A Survey

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
The Internet ◽  
Fake News ◽  
Fact Checking ◽  
The Many ◽  
Human Powered ◽  
The Web

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.

The Future of Instruction Modeling

2020 ◽  
pp. 205-228
Author(s):  
George A. Khachatryan
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Foreign Language ◽  
Natural Language ◽  
Blended Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Government Policies ◽  
Free Form ◽  
Learning Programs

Instruction modeling is still in its early stages. This chapter discusses promising directions in which instruction modeling could develop in coming years. This includes increasing the richness of interfaces used in instruction modeling programs (e.g., by allowing students to enter responses in free form and have them graded via natural language processing); applying instruction modeling to subjects beyond mathematics, including English, foreign language, and science; using educational data mining to create automated “coaches” to help teachers better implement instruction modeling programs in their classrooms; creating approaches to instruction modeling that allow for rapid authorship of content; redesigning schools (in schedules as well as architecture) to optimize the use of instruction modeling; and putting in place government policies to encourage the use of comprehensive blended learning programs (such as those developed through instruction modeling).

scholarly journals Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods

Cancer ◽  
2016 ◽  
Vol 123 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Tejal A. Patel ◽  
Mamta Puppala ◽  
Richard O. Ogunti ◽  
Joe E. Ensor ◽  
Tiancheng He ◽  
...  
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Clinical Decision ◽  
Pathologic Findings ◽  
Mining Methods

Computer Science Meets Education: Natural Language Processing for Automatic Grading of Open-Ended Questions in eBooks

2020 ◽  
Vol 58 (7) ◽  
pp. 1227-1255
Author(s):  
Glenn Gordon Smith ◽  
Robert Haworth ◽  
Slavko Žitnik
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Arts ◽  
Computer Science ◽  
Language Processing ◽  
Real World ◽  
Fourth Grade ◽  
Science Students ◽  
Web Based ◽  
Science Professor

We investigated how Natural Language Processing (NLP) algorithms could automatically grade answers to open-ended inference questions in web-based eBooks. This is a component of research on making reading more motivating to children and to increasing their comprehension. We obtained and graded a set of answers to open-ended questions embedded in a fiction novel written in English. Computer science students used a subset of the graded answers to develop algorithms designed to grade new answers to the questions. The algorithms utilized the story text, existing graded answers for a given question and publicly accessible databases in grading new responses. A computer science professor used another subset of the graded answers to evaluate the students’ NLP algorithms and to select the best algorithm. The results showed that the best algorithm correctly graded approximately 85% of the real-world answers as correct, partly correct, or wrong. The best NLP algorithm was trained with questions and graded answers from a series of new text narratives in another language, Slovenian. The resulting NLP algorithm model was successfully used in fourth-grade language arts classes for providing feedback to student answers on open-ended questions in eBooks.

COMPUTATIONAL HUMANITIES

2018 ◽  
Vol 8 (6) ◽  
pp. 45
Author(s):  
NANA AMPAH ◽  
Matthew Sadiku ◽  
Omonowo Momoh ◽  
Sarhan Musa
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Analysis Data ◽  
Large Database ◽  
Database Analysis ◽  
Computational Tools ◽  
The Past ◽  
Network Mapping

Computational humanities is at the intersection of computing technologies and the disciplines of the humanities. Research in this field has steadily increased over the past years. Computational tools supporting textual search, large database analysis, data mining, network mapping, and natural language processing are employed by the humanities researcher.  This opens up new realms for analysis and understanding.  This paper provides a brief introduction into computational humanities.

Natural Language Processing and Biological Methods

2011 ◽  
pp. 1173-1178
Author(s):  
Gemma Bel Enguix ◽  
M. Dolores Jiménez López
Keyword(s):  
Natural Language Processing ◽  
Molecular Biology ◽  
Natural Language ◽  
Computer Science ◽  
Genetic Code ◽  
20Th Century ◽  
Language Processing ◽  
Dna Sequences ◽  
Theoretical Computer Science ◽  
Automated Generation

During the 20th century, biology—especially molecular biology—has become a pilot science, so that many disciplines have formulated their theories under models taken from biology. Computer science has become almost a bio-inspired field thanks to the great development of natural computing and DNA computing. From linguistics, interactions with biology have not been frequent during the 20th century. Nevertheless, because of the “linguistic” consideration of the genetic code, molecular biology has taken several models from formal language theory in order to explain the structure and working of DNA. Such attempts have been focused in the design of grammar-based approaches to define a combinatorics in protein and DNA sequences (Searls, 1993). Also linguistics of natural language has made some contributions in this field by means of Collado (1989), who applied generativist approaches to the analysis of the genetic code. On the other hand, and only from theoretical interest a strictly, several attempts of establishing structural parallelisms between DNA sequences and verbal language have been performed (Jakobson, 1973, Marcus, 1998, Ji, 2002). However, there is a lack of theory on the attempt of explaining the structure of human language from the results of the semiosis of the genetic code. And this is probably the only arrow that remains incomplete in order to close the path between computer science, molecular biology, biosemiotics and linguistics. Natural Language Processing (NLP) –a subfield of Artificial Intelligence that concerns the automated generation and understanding of natural languages— can take great advantage of the structural and “semantic” similarities between those codes. Specifically, taking the systemic code units and methods of combination of the genetic code, the methods of such entity can be translated to the study of natural language. Therefore, NLP could become another “bio-inspired” science, by means of theoretical computer science, that provides the theoretical tools and formalizations which are necessary for approaching such exchange of methodology. In this way, we obtain a theoretical framework where biology, NLP and computer science exchange methods and interact, thanks to the semiotic parallelism between the genetic code and natural language.

DATA MINING WITH NATURAL LANGUAGE PROCESSING AND CORPUS LINGUISTICS

2016 ◽  
pp. 255-275 ◽  
Author(s):  
Alison L. Bailey ◽  
Anne Blackstock-Bernstein ◽  
Eve Ryan ◽  
Despina Pitsoulakis
Keyword(s):  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Corpus Linguistics

scholarly journals Review On: Detection of Spam Comments Using NLP Algorithm

2018 ◽  
Vol 7 (01) ◽  
pp. 23386-23489
Author(s):  
Miss Rohini D.Warkar ◽  
Mr.I.R. Shaikh
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Social Networking ◽  
Real Time ◽  
Social Services ◽  
Language Processing ◽  
Social Networking Sites ◽  
Online Media

Detecting trending topics is perfect to summarize information getting from social media. To extract what topic is becoming hot on online media is one of the challenges. As we considering social media so social services are opportunity for spamming which greatly affect on value of real time search. Therefore the next task is to control spamming from social networking sites. For completing these challenges different concepts of data mining will be used. For now whatever work has been done is narrated below like spam control using natural language processing for preprocessing and clustering. One account has been created for making it real.

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