The Use of Natural Language Processing Approach for Converting Pseudo Code to C# Code

2019 ◽  
Vol 29 (1) ◽  
pp. 1388-1407 ◽  
Author(s):  
Ayad Tareq Imam ◽  
Ayman Jameel Alnsour
Keyword(s):  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Programming Language ◽  
Language Processing ◽  
Semantic Role Labeling ◽  
Code Generator ◽  
Computer Aided ◽  
Automatic Code ◽  
Computer Aided Software Engineering

Abstract Although current computer-aided software engineering tools support developers in composing a program, there is no doubt that more flexible supportive tools are needed to address the increases in the complexity of programs. This need can be met by automating the intellectual activities that are carried out by humans when composing a program. This paper aims to automate the composition of a programming language code from pseudocode, which is viewed here as a translation process for a natural language text, as pseudocode is a formatted text in natural English language. Based on this view, a new automatic code generator is developed that can convert pseudocode to C# programming language code. This new automatic code generator (ACG), which is called CodeComposer, uses natural language processing (NLP) techniques such as verb classification, thematic roles, and semantic role labeling (SRL) to analyze the pseudocode. The resulting analysis of linguistic information from these techniques is used by a semantic rule-based mapping machine to perform the composition process. CodeComposer can be viewed as an intelligent computer-aided software engineering (I_CASE) tool. An evaluation of the accuracy of CodeComposer using a binomial technique shows that it has a precision of 88%, a recall of 91%, and an F-measure of 89%.

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.

English spoken stress recognition based on natural language processing and endpoint detection algorithm

2021 ◽  
pp. 002072092098353
Author(s):  
Tieyan Yue
Keyword(s):  
Fractal Dimension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Detection Algorithm ◽  
Endpoint Detection ◽  
Time Frequency ◽  
Language System ◽  
Computer Aided ◽  
Speech Feature

Nowadays, there are more and more researches on the application of natural language processing technology in computer-aided language system, which can provide a good assistant role for foreign language learners. However, in the research of computer-aided language system, there are still some deficiencies in the recognition of English spoken stress nodes, which cannot be well recognized. Based on this, this paper proposes a method of English spoken accent recognition based on natural language processing and endpoint detection algorithm, which aims to promote the accuracy of accent recognition in the computer-aided language system and improve the performance of the computer-aided language system. In order to avoid the interference of background noise, this paper proposes a short-term time-frequency endpoint detection algorithm which can accurately judge the beginning and end of speech in complex environment. Then, on the basis of traditional speech feature extraction and fractal dimension theory, a nonlinear fractal dimension speech feature is extracted. Finally, RankNet is used to process the extracted features to realize the recognition of English spoken stress nodes. In the simulation analysis, the application effect of the short-term time-frequency endpoint detection algorithm proposed in this paper in the complex background noise and the effect of non-linear fractal dimension speech features on the recognition of English spoken stress nodes are verified. Finally, the performance and good application effect of the method designed in this paper are illustrated.

scholarly journals Implementing WordNet Measures of Lexical Semantic Similarity in a Fuzzy Logic Programming System

2021 ◽  
pp. 1-19
Author(s):  
PASCUAL JULIÁN-IRANZO ◽  
FERNANDO SÁENZ-PÉREZ
Keyword(s):  
Fuzzy Logic ◽  
Natural Language Processing ◽  
Natural Language ◽  
Logic Programming ◽  
Programming Language ◽  
Language Processing ◽  
Similarity Measures ◽  
Programming System ◽  
Syntactic Structures ◽  
Unification Algorithm

Abstarct This paper introduces techniques to integrate WordNet into a Fuzzy Logic Programming system. Since WordNet relates words but does not give graded information on the relation between them, we have implemented standard similarity measures and new directives allowing the proximity equations linking two words to be generated with an approximation degree. Proximity equations are the key syntactic structures which, in addition to a weak unification algorithm, make a flexible query-answering process possible in this kind of programming language. This addition widens the scope of Fuzzy Logic Programming, allowing certain forms of lexical reasoning, and reinforcing Natural Language Processing (NLP) applications.

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