Formalizing Natural Languages: Applications to Natural Language Processing and Digital Humanities

2021 ◽  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Digital Humanities ◽  
Natural Languages

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 MLGrafViz: multilingual ontology visualization plug-in for protégé

2021 ◽  
Vol 2 (1) ◽  
pp. 43-48
Author(s):  
Merlin Florrence
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Knowledge Based Systems ◽  
Natural Languages ◽  
The Core ◽  
Knowledge Based ◽  
Ontology Visualization ◽  
Language User ◽  
Core Ontology

Natural Language Processing (NLP) is rapidly increasing in all domains of knowledge acquisition to facilitate different language user. It is required to develop knowledge based NLP systems to provide better results.  Knowledge based systems can be implemented using ontologies where ontology is a collection of terms and concepts arranged taxonomically.  The concepts that are visualized graphically are more understandable than in the text form.   In this research paper, new multilingual ontology visualization plug-in MLGrafViz is developed to visualize ontologies in different natural languages. This plug-in is developed for protégé ontology editor. This plug-in allows the user to translate and visualize the core ontology into 135 languages.

scholarly journals Toward Requirements and Design Traceability Using Natural Language Processing

2018 ◽  
Vol 3 (7) ◽  
pp. 42 ◽  
Author(s):  
Omer Salih Dawood ◽  
Abd-El-Kader Sahraoui
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Unified Modeling Language ◽  
Rule Engine ◽  
Natural Languages ◽  
Unified Modeling ◽  
Requirement Document ◽  
Different Levels

The paper aimed to address the problem of incompleteness and inconsistency between requirements and design stages, and how to make efficient linking between these stages. Software requirements written in natural languages (NL), Natural Language Processing (NLP) can be used to process requirements. In our research we built a framework that can be used to generate design diagrams from requirements in semi-automatic way, and make traceability between requirements and design phases, and in contrast. Also framework shows how to manage traceability in different levels, and how to apply changes to different artifacts. Many traceability reports can be generated based on developed framework. After Appling this model we obtained good results. Based on our case study the model generate a class diagram depends on central rule engine, and traceability was built and can be managed in visualize manner. We proposed to continue this research as its very critical area by adding more Unified Modeling Language(UML) diagrams, and apply changes directly inside software requirement document.

scholarly journals Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

2019 ◽  
Vol 48 (3) ◽  
pp. 432-445 ◽  
Author(s):  
Laszlo Toth ◽  
Laszlo Vidacs
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Software Systems ◽  
Functional Requirements ◽  
Natural Languages ◽  
System Analyst

Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients rarely emphasize these specifications as much as they might be justified. Identifying, classifying and reconciling the requirements is one of the main duty of the System Analyst, which task, without using a proper tool, can be very demanding and time-consuming. Tools which support text processing are expected to improve the accuracy of identification and classification of requirements even in an unstructured set of inputs. System Analysts can use them also in document archeology tasks where many documents, regulations, standards, etc. have to be processed. Methods elaborated in natural language processing and machine learning offer a solid basis, however, their usability and the possibility to improve the performance utilizing the specific knowledge from the domain of the software engineering are to be examined thoroughly. In this paper, we present the results of our work adapting natural language processing and machine learning methods for handling and transforming textual inputs of software development. The major contribution of our work is providing a comparison of the performance and applicability of the state-of-the-art techniques used in natural language processing and machine learning in software engineering. Based on the results of our experiments, tools can be designed which can support System Analysts working on textual inputs.

scholarly journals Detection of Email Spam using Natural Language Processing Based Random Forest Approach

2021 ◽  
Author(s):  
Alanazi Rayan ◽  
Ahmed I. Taloba
Keyword(s):  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
The Internet ◽  
Natural Languages ◽  
Efficient Detection ◽  
Random Node ◽  
A Company ◽  
Email Spam

Abstract An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those temporary email addresses for sending free spam emails without revealing the spammers real account details. These attacks create major problems like theft of user credentials, lack of storage, etc. Hence it is essential to introduce an efficient detection mechanismthrough feature extraction and classification for detecting spam emails and temporary email addresses. This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach. With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.

scholarly journals Sentiment analysis of customer reviews in zomato bangalore restaurants using random forest classifier

2019 ◽  
Vol 7 (1) ◽  
pp. 1831-1840
Author(s):  
Bern Jonathan ◽  
Jay Idoan Sihotang ◽  
Stanley Martin
Keyword(s):  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Natural Languages ◽  
Inverse Document Frequency ◽  
Customer Reviews ◽  
Document Frequency ◽  
Split Test

Introduction: Natural Language Processing is one part of Artificial Intelligence and Machine Learning to make an understanding of the interactions between computers and human (natural) languages. Sentiment analysis is one part of Natural Language Processing, that often used to analyze words based on the patterns of people in writing to find positive, negative, or neutral sentiments. Sentiment analysis is useful for knowing how users like something or not. Zomato is an application for rating restaurants. The rating has a review of the restaurant which can be used for sentiment analysis. Based on this, writers want to discuss the sentiment of the review to be predicted. Method: The method used for preprocessing the review is to make all words lowercase, tokenization, remove numbers and punctuation, stop words, and lemmatization. Then after that, we create word to vector with the term frequency-inverse document frequency (TF-IDF). The data that we process are 150,000 reviews. After that make positive with reviews that have a rating of 3 and above, negative with reviews that have a rating of 3 and below, and neutral who have a rating of 3. The author uses Split Test, 80% Data Training and 20% Data Testing. The metrics used to determine random forest classifiers are precision, recall, and accuracy. The accuracy of this research is 92%. Result: The precision of positive, negative, and neutral sentiment is 92%, 93%, 96%. The recall of positive, negative, and neutral sentiment are 99%, 89%, 73%. Average precision and recall are 93% and 87%. The 10 words that affect the results are: “bad”, “good”, “average”, “best”, “place”, “love”, “order”, “food”, “try”, and “nice”.

scholarly journals The Lattice-Valued Turing Machines and the Lattice-Valued Type 0 Grammars

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Juan Tang ◽  
Yong Fang ◽  
Jian-Gang Tang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Turing Machine ◽  
Language Processing ◽  
Dynamic Characteristics ◽  
Design Methodology ◽  
Turing Machines ◽  
Natural Languages ◽  
New Approach ◽  
Dynamic Transformation

Purpose.The purpose of this paper is to study a class of the natural languages called the lattice-valued phrase structure languages, which can be generated by the lattice-valued type 0 grammars and recognized by the lattice-valued Turing machines.Design/Methodology/Approach.From the characteristic of natural language, this paper puts forward a new concept of the l-valued Turing machine. It can be used to characterize recognition, natural language processing, and dynamic characteristics.Findings.The mechanisms of both the generation of grammars for the lattice-valued type 0 grammar and the dynamic transformation of the lattice-valued Turing machines were given.Originality/Value.This paper gives a new approach to study a class of natural languages by using lattice-valued logic theory.

scholarly journals Analysis of Neural Network Based Language Modeling

2020 ◽  
Vol 2 (1) ◽  
pp. 53-63
Author(s):  
Dr. Karrupusamy P.
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Network Models ◽  
Research Field ◽  
Neural Network Models ◽  
Natural Languages ◽  
The Neural Network ◽  
Language Modelling

The fundamental and core process of the natural language processing is the language modelling usually referred as the statistical language modelling. The language modelling is also considered to be vital in the processing the natural languages as the other chores such as the completion of sentences, recognition of speech automatically, translations of the statistical machines, and generation of text and so on. The success of the viable natural language processing totally relies on the quality of the modelling of the language. In the previous spans the research field such as the linguistics, psychology, speech recognition, data compression, neuroscience, machine translation etc. As the neural network are the very good choices for having a quality language modelling the paper presents the analysis of neural networks in the modelling of the language. Utilizing some of the dataset such as the Penn Tree bank, Billion Word Benchmark and the Wiki Test the neural network models are evaluated on the basis of the word error rate, perplexity and the bilingual evaluation under study scores to identify the optimal model.

scholarly journals A Systematic Literature Review of Hausa Natural Language Processing

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Rufai Yusuf Zakari ◽  
Zaharaddeen Karami Lawal ◽  
Idris Abdulmumin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Name Entity Recognition ◽  
Entity Recognition ◽  
Natural Languages ◽  
Systematic Analysis ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Intelligent Models

The processing of natural languages is an area of computer science that has gained growing attention recently. NLP helps computers recognize, in other words, the ways in which people use their language. NLP research, however, has been performed predominantly on languages with abundant quantities of annotated data, such as English, French, German and Arabic. While the Hausa Language is Africa's second most commonly used language, only a few studies have so far focused on Hausa Natural Language Processing (HNLP). In this research paper, using a keyword index and article title search, we present a systematic analysis of the current literature applicable to HNLP in the Google Scholar database from 2015 to June 2020. A very few research papers on HNLP research, especially in areas such as part-of-speech tagging (POS), Name Entity Recognition (NER), Words Embedding, Speech Recognition and Machine Translation, have just recently been released. This is due to the fact that for training intelligent models, NLP depends on a huge amount of human-annotated data. HNLP is now attracting researchers' attention after extensive research on NLP in English and other languages has been performed. The key objectives of this paper are to promote research, to define likely areas for future studies in the HNLP, and to assist in the creation of further examinations by researchers for relevant studies.

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