A semi-automated approach for generating sequence diagrams from Arabic user requirements using a natural language processing tool

2017 ◽  
Author(s):  
Nermeen Alami ◽  
Nabil Arman ◽  
Faisal Khamyseh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
User Requirements ◽  
Sequence Diagrams ◽  
Natural Language Processing Tool ◽  
Generating Sequence

Generating Use Case Models from Arabic User Requirements in a Semiautomated Approach Using a Natural Language Processing Tool

2015 ◽  
Vol 24 (2) ◽  
pp. 277-286 ◽  
Author(s):  
Nabil Arman ◽  
Sari Jabbarin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Unified Modeling Language ◽  
Object Oriented ◽  
Software Systems ◽  
User Requirements ◽  
Use Case ◽  
Use Case Models ◽  
Natural Language Processing Tool

AbstractAutomated software engineering has attracted a large amount of research efforts. The use of object-oriented methods for software systems development has made it necessary to develop approaches that automate the construction of different Unified Modeling Language (UML) models in a semiautomated approach from textual user requirements. UML use case models represent an essential artifact that provides a perspective of the system under analysis or development. The development of such use case models is very crucial in an object-oriented development method. The main principles used in obtaining these models are described. A natural language processing tool is used to parse different statements of the user requirements written in Arabic to obtain lists of nouns, noun phrases, verbs, verb phrases, etc., that aid in finding potential actors and use cases. A set of steps that represent our approach for constructing a use case model are presented. Finally, the proposed approach is validated using an experiment involving a group of graduate students who are familiar with use case modeling.

scholarly journals Generating Sequence Diagrams from Arabic User Requirements using MADA+TOKAN Tool

2019 ◽  
pp. 65-72
Author(s):  
Nermeen Alami ◽  
Nabil Arman ◽  
Faisal Khamayseh
Keyword(s):  
Language Processing ◽  
Sequence Diagram ◽  
User Requirements ◽  
Sequence Diagrams ◽  
Part Of Speech ◽  
Novel Approach ◽  
Extensible Markup ◽  
Natural Language Processing Tool ◽  
Generating Sequence ◽  
Flow Transitions

A new semi-automated approach for generating sequence diagrams from Arabic user requirements is presented. In this novel approach, the Arabic user requirements are parsed using a natural language processing tool called MADA+TOKAN to generate the Part Of Speech (POS) tags of the parsed user requirements, then a set of heuristics are applied on the resulted tags to obtain the sequence diagram components; objects, messages and work flow transitions (messages). The generated sequence diagram is expressed using Extensible Markup Language (XMI) to be drawn using sequence diagrams drawing tools. Our approach achieves better results than students in generating sequence diagrams. It also has better accuracy in generating the participants and less accuracy in generating messages exchanged between participants. The proposed approach is validated using a set of experiments involving a set of real cases evaluated by a group of software engineers and a group of graduate students who are familiar with sequence diagrams

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

2021 ◽  
Author(s):  
Xinxu Shen ◽  
Troy Houser ◽  
David Victor Smith ◽  
Vishnu P. Murty
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Individual Difference ◽  
Language Processing ◽  
Large Scale ◽  
High Reliability ◽  
Difference Analysis ◽  
Universal Sentence ◽  
Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

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