scholarly journals Trends In Natural Language Processing : Scope And Challenges

2021 ◽  
pp. 393-401
Author(s):  
Sagarmoy Ganguly ◽  
Asoke Nath
Keyword(s):  
Quantum Mechanics ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Quantum Cryptography ◽  
Language Processing ◽  
Quantum Key Distribution ◽  
Quantum Physics ◽  
Secure Communications ◽  
Practical Application

Quantum cryptography is a comparatively new and special type of cryptography which uses Quantum mechanics to provide unreal protection of data/information and unconditionally secure communications. This is achieved with Quantum Key Distribution (QKD) protocols which is a representation of an essential practical application of Quantum Computation. In this paper the authors will venture the concept of QKD by reviewinghow QKD works, the authors shall take a look at few protocols of QKD, followed by a practical example of Quantum Cryptography using QKD and certain limitations from the perspective of Computer Science in specific and Quantum Physics in general.

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.

scholarly journals NLP commercialisation in the last 25 years

2019 ◽  
Vol 25 (3) ◽  
pp. 419-426
Author(s):  
Robert Dale
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Practical Application ◽  
Quarter Century ◽  
Language Engineering

AbstractThe Journal of Natural Language Engineering is now in its 25th year. The editorial preface to the first issue emphasised that the focus of the journal was to be on the practical application of natural language processing (NLP) technologies: the time was ripe for a serious publication that helped encourage research ideas to find their way into real products. The commercialisation of NLP technologies had already started by that point, but things have advanced tremendously over the last quarter-century. So, to celebrate the journal’s anniversary, we look at how commercial NLP products have developed over the last 25 years.

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.

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.

scholarly journals Natural Language Processing (NLP) in Qualitative Public Health Research: A Proof of Concept Study

2019 ◽  
Vol 18 ◽  
pp. 160940691988702 ◽  
Author(s):  
William Leeson ◽  
Adam Resnick ◽  
Daniel Alexander ◽  
John Rovers
Keyword(s):  
Public Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Qualitative Analysis ◽  
Computer Science ◽  
Language Processing ◽  
Topic Modeling ◽  
Qualitative Data ◽  
Proof Of Concept ◽  
Open Coding

Qualitative data-analysis methods provide thick, rich descriptions of subjects’ thoughts, feelings, and lived experiences but may be time-consuming, labor-intensive, or prone to bias. Natural language processing (NLP) is a machine learning technique from computer science that uses algorithms to analyze textual data. NLP allows processing of large amounts of data almost instantaneously. As researchers become conversant with NLP, it is becoming more frequently employed outside of computer science and shows promise as a tool to analyze qualitative data in public health. This is a proof of concept paper to evaluate the potential of NLP to analyze qualitative data. Specifically, we ask if NLP can support conventional qualitative analysis, and if so, what its role is. We compared a qualitative method of open coding with two forms of NLP, Topic Modeling, and Word2Vec to analyze transcripts from interviews conducted in rural Belize querying men about their health needs. All three methods returned a series of terms that captured ideas and concepts in subjects’ responses to interview questions. Open coding returned 5–10 words or short phrases for each question. Topic Modeling returned a series of word-probability pairs that quantified how well a word captured the topic of a response. Word2Vec returned a list of words for each interview question ordered by which words were predicted to best capture the meaning of the passage. For most interview questions, all three methods returned conceptually similar results. NLP may be a useful adjunct to qualitative analysis. NLP may be performed after data have undergone open coding as a check on the accuracy of the codes. Alternatively, researchers can perform NLP prior to open coding and use the results to guide their creation of their codebook.

Natural Language Processing Tools

2012 ◽  
pp. 9-23 ◽  
Author(s):  
Justin F. Brunelle ◽  
Chutima Boonthum-Denecke
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Open Source ◽  
Computational Linguistics ◽  
Language Processing ◽  
Open Source Software

This chapter discusses a subset of Natural Language Processing (NLP) tools available for researchers and enthusiasts of computer science, computational linguistics, and other fields that may utilize or benefit from Natural Language Processing. Several tools are described in this chapter, along with background, algorithms used in brief, usages, and examples. While this chapter is not comprehensive, it provides an extensive exposure to various NLP tools through examples, and it aims at providing an overview of the resources available, and concentrates mainly on open-source applications. Open-source applications were chosen since they are freely available for download by all users. Commonly, open source software provides the code that makes up the tool, and allows for users to inspect the inner-workings of the tools, or even modify them. By using open source examples, readers of this chapter can extend their investigation of NLP tools beyond the pages of this text by investigating the tools outlined.

scholarly journals Intelligent Essay Grading System using Hybrid Text Processing Techniques

2020 ◽  
pp. 229-235
Author(s):  
Kuyoro S.O. ◽  
Eluwa J. M. ◽  
Akinsola J.E.T ◽  
Ayankoya F.Y. ◽  
Omotunde A.A. ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
Loss Function ◽  
Semantic Analysis ◽  
High Reliability ◽  
Grading System ◽  
Part Of Speech ◽  
Essay Grading

Educational Institutions are facing enormous tasks of marking and grading students at the end of every examination within the shortest possible time. Marking theoretical essay questions which involves thousands of examinees can be biased, subjective and time-consuming, leading to variation in grades awarded by different human assessors. This study presents an Essay Grading System called Intelligent Natural Language Processing Essay Grading System (iNLPEGS) with high accuracy percentage and minimal loss function for scoring assessment that can accommodate more robust questions. Secondary dataset collected from Kaggle provided by The Hewlett Foundation was used to aid semantic analysis and Part of Speech tagging. Assemblage of Computer Science questions and answers were collected from Babcock University Computer Science Department to create a more robust dataset to ensure high reliability. An Intelligent Natural Language Processing Essay Grading Model was designed based on Enhanced Latent Semantic Analysis using Part of Speech n-gram Inverse Document Frequency. Web based application was developed using Django, Gensim, Jupyter Notebook and Anaconda as the development tools due to availability of several Python libraries with SQLite as the database. Results of performance evaluation of iNLPEGS showed accuracy of 89.03% and error of 10.97% connoting that there is very little difference between scores from the developed intelligent essay grading system and a human grader. Also, the loss function from Root Mean Square Error (RSME) showed value of 0.620 which is very small and thus signifies closeness to the line of best fit from the regression equation.

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