## 2 Background, Materials, and Methods  
Software engineers have been exploring various supervised and unsupervised approaches for information extraction. In supervised approaches, syntactical patterns are defined [42], and text is manually scanned for such patterns. In unsupervised information extraction, the machine does the pattern matching. Supervised approaches are reliable but slow. Contrarily, the faster, unsupervised approaches are difficult and prone to errors, mainly due to word sense ambiguation [43]. A purely syntactical analysis cannot capture the nuanced meaning of texts, which is often the culprit of the problem. Recently, pattern matching also involves semantic analysis. External databases of hierarchically structured words such as WordNet or VerbNet [44] and machine learning tools are increasingly used for understanding semantics for reduced word sense ambiguity [45; 46]. NLP toolkits are increasingly used for unsupervised pattern matching and information extraction [47; 48; 49; 50; 51; 52]. Tools such as lemmatizing, tokenizing, stemming, and part-of-speech tagging [53] are helpful for syntactic information extraction. These tools can normalize texts and identify subjects and main verbs from their sentences. The ability to convert highly unstructured texts to structured information through predominantly unsupervised approaches is one of the main advantages of NLP in qualitative data analysis. NLP efficiently analyzes intertextual relationships using syntactic and semantics algorithms. Approaches such as word co-occurrence statistics and sentiment analysis [54] are beneficial for domain modeling [42] and for exploring contextual and behavioral information from textual data. Similarly, its efficiency in pattern matching for information extraction is essential for model development. Although present for decades in object-oriented programming and database development [55; 56], NLP has a minimal footprint in ABM development. The introduction of NLP in ABM development is very recent. [PERSON]. [14; 57] used NLP to model human cognition through word embedding, which is a contextually analyzed vector representation of a text. The procedure places closely related texts next to each other. Specifically, placing agents with similar worldviews together to support theorizing agent decision-making. Although their approach helps develop agent decision-making, it is not well-equipped for developing a comprehensive agent architecture. Another example is the study by [58], who applied NLP in conjunction with machine learning to create an ABM structure from unstructured textual data. In their framework, texts are translated to the agent-attribute-rule framework. They define agents as nouns (e.g., person and place) that perform some actions and attributes as words that represent some variables. Similarly, sentences containing agents or attributes and action verbs are considered rules. The primary goal of their approach is to create an ABM structure mainly for communicating the model to non-modelers. As with machine learning approaches in general, [PERSON] et al. 's approach required a large amount of training data. They used ten highly concise, formally written ABM descriptions from published journals as training datasets. Another limitation, according to the researchers, was the lack of precise distinctions between agents and attributes, attributes could also be nouns, which might confuse the machine. Repeated training with extensive data effectively increased the accuracy of the agent-attribute rule detection. However, the model frequently resulted in underpredictions and overpredictions. Our work is of significant importance in the context of ABM development, particularly in relation to the utilization of machine learning and artificial intelligence. The recent research papers, refs. [30; 59; 60], also emphasized the role of these technologies in this domain. Ref.