scholarly journals Utterance and Objective: Issues in Natural Language Communication

AI Magazine ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 11 ◽  
Author(s):  
Barbara J. Grosz
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computer Systems ◽  
Natural Languages ◽  
What Is Said ◽  
Language Communication ◽  
Natural Language Communication ◽  
The Relationship ◽  
Meaningful Sense

Two premises, reflected in the title, underlie the perspective from which I will consider research in natural language processing in this article. First, progress on building computer systems that process natural languages in any meaningful sense (i.e., systems that interact reasonably with people in natural language) requires considering language as part of a larger communicative situation. Second, as the phrase “utterance and objective” suggests, regarding language as communication requires consideration of what is said literally, what is intended, and the relationship between the two.

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 Getting in Shape: Word Embedding SubSpaces

2019 ◽  
Author(s):  
Tianyuan Zhou ◽  
João Sedoc ◽  
Jordan Rodu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Theoretical Framework ◽  
Word Embedding ◽  
Word Embeddings ◽  
Empirical Results ◽  
Linear Alignment ◽  
The Relationship

Many tasks in natural language processing require the alignment of word embeddings. Embedding alignment relies on the geometric properties of the manifold of word vectors. This paper focuses on supervised linear alignment and studies the relationship between the shape of the target embedding. We assess the performance of aligned word vectors on semantic similarity tasks and find that the isotropy of the target embedding is critical to the alignment. Furthermore, aligning with an isotropic noise can deliver satisfactory results. We provide a theoretical framework and guarantees which aid in the understanding of empirical results.

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 APMorph: finite-state transducer for Amazigh pronominal morphology

2021 ◽  
Vol 11 (1) ◽  
pp. 699
Author(s):  
Rachid Ammari ◽  
Ahbib Zenkoua
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Automatic Processing ◽  
Finite State ◽  
Finite State Transducer ◽  
The Relationship

Our work aims to present an amazigh pronominal morphological analyzer (APMorph) based on xerox’s finite-state transducer (XFST). Our system revolves around a large lexicon named “APlex” including the affixed pronoun to the noun and to the verb and the characteristics relating to each lemma. A set of rules are added to define the inflectional behavior and morphosyntactic links of each entry as well as the relationship between the different lexical units. The implementation and the evaluation of our approach will be detailed within this article. The use of XFST remains a relevant choice in the sense that this platform allows both analysis and generation. The robustness of our system makes it able to be integrated in other applications of natural language processing (NLP) especially spellchecking, machine translation, and machine learning. This paper presents a continuation of our previous works on the automatic processing of Amazigh nouns and verbs.

Are Atypical Things More Popular?

2018 ◽  
Vol 29 (7) ◽  
pp. 1178-1184 ◽  
Author(s):  
Jonah Berger ◽  
Grant Packard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cultural Dynamics ◽  
The Relationship ◽  
Shed Light

Why do some cultural items become popular? Although some researchers have argued that success is random, we suggest that how similar items are to each other plays an important role. Using natural language processing of thousands of songs, we examined the relationship between lyrical differentiation (i.e., atypicality) and song popularity. Results indicated that the more different a song’s lyrics are from its genre, the more popular it becomes. This relationship is weaker in genres where lyrics matter less (e.g., dance) or where differentiation matters less (e.g., pop) and occurs for lyrical topics but not style. The results shed light on cultural dynamics, why things become popular, and the psychological foundations of culture more broadly.

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”.

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