Ontology Formation and Comparison for Syllabus Structure Using NLP

2021 ◽  
Author(s):  
Masoom Raza ◽  
Aditee Patil ◽  
Mangesh Bedekar ◽  
Rashmi Phalnikar ◽  
Bhavana Tiple
Keyword(s):  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Comparative Study ◽  
Language Processing ◽  
Shared Knowledge ◽  
The Subject ◽  
The Creation ◽  
The Right ◽  
Collection And Management

Ontologies are largely responsible for the creation of a framework or taxonomy for a particular domain which represents the shared knowledge, concepts and how these concepts are related with each other. This paper shows the usage of ontology for the comparison of a syllabus structure of universities. This is done with the extraction of the syllabus, creation of ontology for the representing syllabus, then parsing the ontology and applying Natural language processing to remove unwanted information. After getting the appropriate ontologies, a comparative study is made on them. Restrictions are made over the extracted syllabus to the subject “Software Engineering” for convenience. This depicts the collection and management of ontology knowledge and processing it in the right manner to get the desired insights.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

scholarly journals Natural Language Processing by Enhanced Honey Encryption Technique

2019 ◽  
Vol 8 (12S) ◽  
pp. 159-163
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cyber Attacks ◽  
Binary Form ◽  
Brute Force ◽  
Natural Languages ◽  
Cipher Text ◽  
The Right ◽  
Binary Strings

Traditional encryption systems and techniques have always been vulnerable to brute force cyber-attacks. This is due to bytes encoding of characters utf8 also known as ASCII characters. Therefore, an opponent who intercepts a cipher text and attempts to decrypt the signal by applying brute force with a faulty pass key can detect some of the decrypted signals by employing a mixture of symbols that are not uniformly dispersed and contain no meaningful significance. Honey encoding technique is suggested to curb this classical authentication weakness by developing cipher-texts that provide correct and evenly dispersed but untrue plaintexts after decryption with a false key. This technique is only suitable for passkeys and PINs. Its adjustment in order to promote the encoding of the texts of natural languages such as electronic mails, records generated by man, still remained an open-end drawback. Prevailing proposed schemes to expand the encryption of natural language messages schedule exposes fragments of the plaintext embedded with coded data, thus they are more prone to cipher text attacks. In this paper, amending honey encoded system is proposed to promote natural language message encryption. The main aim was to create a framework that would encrypt a signal fully in binary form. As an end result, most binary strings semantically generate the right texts to trick an opponent who tries to decipher an error key in the cipher text. The security of the suggested system is assessed..

scholarly journals APLIKASI AGEN CERDAS UNTUK PEMBELAJARAN PEMPROGRAMAN BERORIENTASI OBJEK BERBASIS NATURAL LANGUAGE PROCESSING (NLP)

2018 ◽  
Author(s):  
Khairil Anam ◽  
SEHMAN
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
High Performance ◽  
Object Oriented Programming ◽  
Learning System ◽  
Computer Laboratory ◽  
Final Project ◽  
Laboratory Learning ◽  
The Creation

The existence of a touch of technology on laboratory learning becomes another alternative as a supporter of laboratory learning. Different practitioner's wishes and intensity of relatively short laboratory practice which resulted in dissatisfaction in the implementation of a practicum. Thus, an intelligent learning alternative is needed. This intelligent learning aims to provide high-quality and high-performance training skills that can assist the practitioner in solving problems related to practicum materials. The intelligent learning system is a learning system that handles some student instruction without any intervention from a teacher.Alternative learning system that can support the creation of Intelligent Learning System is by Natural Language Processing (NLP) method. This final project provides an explanation of the creation and implementation of intelligent learning systems in the Object Oriented Programming Computer Laboratory. This system consists of several stages: parsing, similarity, stemming, Knowledge Base which is designed in an interactive form between praktikan and agent based dialoge based application. The success rate of this system in answering questions from praktikan session II is 88.75%.

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 Non-size increasing graph rewriting for natural language processing

2018 ◽  
Vol 28 (8) ◽  
pp. 1451-1484
Author(s):  
GUILLAUME BONFANTE ◽  
BRUNO GUILLAUME
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Theoretical Aspect ◽  
Graph Rewriting ◽  
Complexity Bounds ◽  
Mathematical Structures ◽  
Rewriting Systems ◽  
The Subject ◽  
Linguistic Structures

A very large amount of work in Natural Language Processing (NLP) use tree structure as the first class citizen mathematical structures to represent linguistic structures, such as parsed sentences or feature structures. However, some linguistic phenomena do not cope properly with trees; for instance, in the sentence ‘Max decides to leave,’ ‘Max’ is the subject of the both predicates ‘to_decide’ and ‘to_leave’. Tree-based linguistic formalisms generally use some encoding to manage sentences like the previous example. In former papers (Bonfante et al. 2011; Guillaume and Perrier 2012), we discussed the interest to use graphs rather than trees to deal with linguistic structures, and we have shown how Graph Rewriting could be used for their processing, for instance in the transformation of the sentence syntax into its semantics. Our experiments have shown that Graph Rewriting applications to NLP do not require the full computational power of the general Graph Rewriting setting. The most important observation is that all graph vertices in the final structures are in some sense ‘predictable’ from the input data, and so we can consider the framework of Non-size increasing Graph Rewriting. In our previous papers, we have formally described the Graph Rewriting calculus we used and our purpose here is to study the theoretical aspect of termination with respect to this calculus. Given that termination is undecidable in general, we define termination criterions based on weight, we prove the termination of weighted rewriting systems, and we give complexity bounds on derivation lengths for these rewriting systems.

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