—Analyzing stakeholder needs and transforming them into requirements is an important early step i n the systems engineering lifecycle [1]. In regulated industries , important technical requirements can be found in state and fe deral laws and regulations. Casino gaming is one such industry. This paper analyzes South Dakota and Nevada slot machine regul ations and applies automated natural language processing to ex tract and analyze technical requirements derived from them. First, each parts of speech (POS) in the regulations is identif ied. From this, the important adjective and noun keywords and keywo rd combinations are extracted using the Rapid Automati c Keyword Extraction (RAKE) algorithm [2]. Next, slot machine requirements are extracted from the gaming laws, ma ny of which lack a “shall” in them. To perform this, a 12-rule pattern matching algorithm that applies phrase substitution s and identifies leader-subordinate paragraph headings is applied to the slot machine gaming rules. This approach successf ully extracts nearly all of the slot machine technical and operat ions requirements, though fails to separate compound req uirements accounting for approximately 3% of the total. Then , after stemming and stopping the regulations, a Naïve Baye s model for identifying functional requirements is constructed from the South Dakota regulations and applied to the Nevada regula tions. This model is able to predict the Nevada functional prod uct requirements from amongst the full set of extracted requirements with 87.5% accuracy. Finally, using a modified ver sion of the Dice similarity metric where the word counts are weighte d by the term frequency-inverse document frequency (TF-IDF) score s, the South Dakota requirements most similar to each of t he Nevada requirements is determined. The paired South Dakot a and Nevada requirements are then assessed using systems engineering expertise for equivalency and relatedness. Using t he geometric mean of sensitivity and specificity as a scoring me tric, the pairing algorithm optimum performance is 96.1% accurate in identifying equivalent requirements between the two sets of reg ulations, and 82.0% accurate in identifying related requirements. Keywords—requirements analysis, similarity, natural language processing, non-functional requirements, slot machi nes. I. INTRODUCTION Stakeholder requirements come from a variety of sou rces, including contract requirements, market conditions, user feedback, and sometimes, laws and regulations. Law s and regulations are a particularly important source of requirements within well-regulated industries such as aerospace and defense, healthcare, finance, automotive and casino gaming. This paper uses automated natural language process ing (NLP) techniques to extract key concepts and requir ements in slot machine gaming regulations, identify which of the extracted requirements are functional (vs. non-functional or operations), and compares slot machine requirements embedded in the gaming laws of different states. An overview of thi s contribution and previous relevant contributions is found in Sec tion II. Gaming statutes from South Dakota and Nevada are us ed for this analysis. Section III summarizes the regulati on contents and provides examples. Section IV discusses the use of part of speech (POS) tagging and the Rapid Automatic Keywor d Extraction (RAKE) algorithm [2] to identify the mos t important words and phrases in the regulation sets, forming t he basis for a program glossary. Preparing the data for requirements analysis is des cribed in section V. Not all requirements embedded in state laws have the word “shall” embedded in them. Section V describes a simple 12-rule pattern matching algorithm which successful ly extracted over 99% of the requirements from the gaming regula tions, although these rules failed to separate out certain types of compound requirements. Automated analyses of the requirements to determine which requirements are fu nctional and which are not is described in section VI, and s ection VII describes the use of similarity analysis to identif y equivalent and related requirements. A summary and opportunities for further research are provided in section VIII. II. CONTRIBUTION SUMMARY A. Previous Contributions The use of NLP for requirements engineering has bee n around for some time. Reference [3] provides a goo d overview of the state of the art for natural language requir ements engineering in 2001, and a more up-to-date (2018) o verview can be found in [4]. Many of the previous NLP approaches to organize requirements included similarity analysis and/or cl assification algorithms. Early examples of similarity analysis to group requirements can be found in [5,6]. More recently, similarity analysis has been used to link requirements to desi gn documents [7]. An application of similarity to derive and cat egorize app requirements from user reviews can be found in [8,9 ]. A description of the use of support vector machines t o classify a variety of NFR types in electronic health systems a nd comparison of their performance to Bayesian analysi s is found in [10]. A succinct overview of recent work in NFR requirements classification is described in [11], t ogether with examples for mathematical software systems. Natural language similarity analysis tools useful f or requirements analysis are described in [12] and [10 ]. ORSIM (OpenReq-SIMilarity), a tool that encapsulates seve ral similarity mechanisms, is described in [12], togeth er with results from its application to a test database under the E uropean Union’s OpenReq project. NFR locator is a tool tha t extracts NFRs from free text documents. It and its applicat ion to health record management is described in [10]. Previous authors have also used similarity measures to compare requirements sets for reuse opportunities [ 13] and product line development [14]. B. This Contribution To date, however, none of these techniques have bee n applied to the casino gaming industry in general, n or used to extract and analyze requirements from gaming laws a nd regulations in particular. The gaming industry is h eavily regulated, and legal statutes for slot machines are an important source of many slot machine product and operations including both functional and non-functional technical requir ements. This paper’s focus is the use of NLP to extract key words and requirements from state gaming regulations, charact erize the requirements as either functional or otherwise, and discover equivalent and similar requirements between the Nev ada and South Dakota gaming regulations. III. DATA DESCRIPTION AND PRE -PROCESSING A. Data Description South Dakota legal statues applying to slot machine s are found in South Dakota Codified Law (SDCL), Chapter 20:18:17, “Slot Machine Requirements” [15]. These r egulations include many mandated functional and non-functional requirements for slot machines and slot machine net works, as well as operations rules that casinos that offer sl ot machines must follow. This document also includes traceabil ity information such as the statute number, the source (referencing where the law is documented in the South Dakota Reg ister record), and