Automated Extraction and Classification of Slot Machine Requirements from Gaming Regulations

Prendergast, Michael

doi:10.1109/syscon48628.2021.9447144

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Text-based regulations are very similar and interesting documents for us because they are giving information about constraints and rules regarding the domain. There is an example from the Game industry about documents called Automated Extraction and Classification of Slot Machine Requirements from Gaming Regulations [30]. The rules from state and federal laws and regulations were extracted using primitive rule-based algorithms and Naive Bayes.…”

Section: Information Extractionmentioning

confidence: 99%

Deriving semantic validation rules from industrial standards: An OPC UA study

Bareedu,

Frühwirth,

Niedermeier

et al. 2024

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Industrial standards provide guidelines for data modeling to ensure interoperability between stakeholders of an industry branch (e.g., robotics). Most frequently, such guidelines are provided in an unstructured format (e.g., pdf documents) which hampers the automated validations of information objects (e.g., data models) that rely on such standards in terms of their compliance with the modeling constraints prescribed by the guidelines. This raises the risk of costly interoperability errors induced by the incorrect use of the standards. There is, therefore, an increased interest in automatic semantic validation of information objects based on industrial standards. In this paper we focus on an approach to semantic validation by formally representing the modeling constraints from unstructured documents as explicit, machine-actionable rules (to be then used for semantic validation) and (semi-)automatically extracting such rules from pdf documents. While our approach aims to be generically applicable, we exemplify an adaptation of the approach in the concrete context of the OPC UA industrial standard, given its large-scale adoption among important industrial stakeholders and the OPC UA internal efforts towards semantic validation. We conclude that (i) it is feasible to represent modeling constraints from the standard specifications as rules, which can be organized in a taxonomy and represented using Semantic Web technologies such as OWL and SPARQL; (ii) we could automatically identify modeling constraints in the specification documents by inspecting the tables ( P = 87 %) and text of these documents (F1 up to 94%); (iii) the translation of the modeling constraints into formal rules could be fully automated when constraints were extracted from tables and required a Human-in-the-loop approach for constraints extracted from text.

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Section: Information Extractionmentioning

confidence: 99%

Deriving semantic validation rules from industrial standards: An OPC UA study

Bareedu,

Frühwirth,

Niedermeier

et al. 2024

View full text Add to dashboard Cite

show abstract

“…The topics extracted are validated by comparing them with the results reported in previous works using supervised learning (which is highly dependent on manual annotation by experts). Prendergast (2021) analyzes South Dakota and Nevada slot machine regulations and applies automated NLP techniques to extract and analyze the resulting technical requirements. This paper propose an approach that includes data preprocessing, FR identification by constructing a Naive Bayes model from South Dakota regulations and applied to Nevada regulations This model predicts functional and non-functional Nevada product requirements from a full set of extracted requirements.…”

Section: Related Workmentioning

confidence: 99%

Extracting feature requests from online reviews of travel industry

Kumari

Memon

2022

Acta Sci Technol

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Before product development, Requirement Engineering (RE) is the fundamental need to know customer preferences for any product. Traditionally, RE is carried out in several ways, particularly by conducting interviews, questionnaires, surveys etc. but these methods provide limited amount of data. As user’s preferences vary from country to country for any type of application, it is very hectic and time consuming to collect user requirements from different countries manually. As the internet is widely used now a days, a large number of customer’s reviews are available online that can be used to obtain the requirements for any product without manual work. Online customer reviews can be divided into three types: user experience, bugs and feature requests. Among these 3 categories, feature requests can be very useful for stakeholders (analysts/ requirements engineers) to acquire the requirements of each application. So, the approach is proposed for feature requests extraction from mobile application reviews of travel industry. In this paper, 4 categories of mobile apps of travel industry belonging to 5 countries have been extracted from Google Play Store and Apple Store. For each category, data from 5 different mobile applications have been considered. Since, the review of users from different countries is in their respective language, those reviews are translated into a standard language i.e. English, and then feature requests from these reviews have been extracted. After that, features are retrieved from user reviews and topic modeling is performed on extracted features since one or more features can be modelled under one topic. To know the opinions of users for any feature request, sentiment analysis is also performed on feature request sentences. These feature requests are also classified as Functional and Non-functional Requirements since it is very useful for application developers to improve or maintain the product to better facilitate the application users

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Automated Extraction and Classification of Slot Machine Requirements from Gaming Regulations

Cited by 2 publications

References 10 publications

Deriving semantic validation rules from industrial standards: An OPC UA study

Deriving semantic validation rules from industrial standards: An OPC UA study

Extracting feature requests from online reviews of travel industry

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