# Generating Process Models by Interacting with Chatbots--A Literature Review
## 5 Reporting of the Systematic Literature Review
### Generating Process Models from Natural Language Text Input (RQ1)  
With research question RQ1, our aim is to elucidate the contemporary modeling technologies and methodologies that are applicable for modeling process models from natural language text input. Out of the 18 studies reviewed on the use of chatbots for process modeling, 15 focused on strategies for creating process models from natural language text input. These studies delineated three distinct methodologies to achieve this objective, as summarized in Table 7. The distribution of the used methods for RQ1 is presented in Figure 4.  
Notably, NLP emerged as the predominant methodology, with 14 out of 15 studies leveraging NLP techniques to facilitate the conversion of natural language text input into structured process models. Additionally, Study S11 [22] mapped rules besides NLP, and Study S16 [27] used sentence-level analysis to accomplish this task. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **2010** & **2011** & **2015** & **2017** & **2018** & **2019** & **2020** & **2021** & **2022** & **2023** & **\# of Studies** \\ \hline Conference & 1 & 1 & 1 & 2 & 1 & 3 & 1 & 1 & 0 & 2 & 13 \\ Journal & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 2 & 1 & 5 \\ Total & 1 & 1 & 1 & 2 & 1 & 3 & 1 & 3 & 2 & 3 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: An overview of the studies publication type and year. \begin{table}
\begin{tabular}{l l} \hline \hline
**Technologies** & **Studies** & **\# of Studies** \\ \hline RQ1_\(A_{1}\)_: NLP & S1 [12], S2 [13], S3 [14], S5 [16], S6 [17], \\  & S8 [19], S9 [20], S10 [21], S11 [22], S12 [23], \\  & S13 [24], S14 [25], S15 [26], S17 [28] & 14 \\ RQ1_\(A_{2}\)_: Mapping Rules & S11 [22] & 1 \\ RQ1_\(A_{3}\)_: Sentence Level Analysis & S16 [27] & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Distribution of the studies by technologies for generating process models from natural language text (RQ1). We provide detailed summaries of the 15 studies in this domain, each elucidating distinct problems, solutions, and methodologies. Through a comprehensive analysis of these contributions, we aim to clarify the commonalities, differences, and emerging trends (cf. Figure 5). In the realm of process modeling, extracting process models manually from textual data poses challenges in terms of time consumption and error susceptibility. However, 'Extracting process models using NLP Technique' [12] pioneers a solution by employing NLP techniques. Through syntactic and semantic analysis, Study S1 automates the extraction process, identifying process elements within text and converting them into structured models. Another hurdle arises in the complex task of creating business process diagrams from textual descriptions. Study S2, 'A Proposed Technique for process modeling Diagram using NLP' [13], tackles this challenge by offering a methodological shift. Leveraging probabilistic latent semantic analysis, the approach developed by study S2 streamlines diagram creation by managing text complexity, effectively translating textual data into comprehensible business process diagrams. Integrating sentiment analysis within business processes for understanding customer feedback presents yet another challenge. Study S3, 'A Combined Model of NLP with process modeling for Sentiment Analysis' [14], confronts this by merging NLP with process modeling. Analyzing textual data within the context of business processes enhances sentiment analysis, thereby improving the accuracy and relevance of insights. Further streamlining the modeling process, study S5, 'Assisted Declarative Process Creation from Natural Language Descriptions' [16], introduces a tool to automate the creation of declarative process models from text. Utilizing NLP, this approach identifies and maps process constraints from textual descriptions, generating declarative process models efficiently.