## 4 Results and Discussion
They, however, pointed out that the contextualized structure did not provide the dynamics of the government and non-government actors. Since the input data only contained interviews from farm households, we failed to capture the dynamics occurring outside of the households. Consequently, the model revealed data gaps where more information needs to be gathered on household food security's government and non-government actors. The proposed unsupervised information extraction picked individual sentences based on their cumulative TFIDF weights. However, some of the individually extracted sentences lacked contextuality and were ambiguous. To add context and reduce this ambiguity, we used neighboring sentences during the unsupervised data extraction and processing phase (Figure 1). We hypothesized that extracting a tuple of preceding and trailing sentences along with the identified sentence can provide vital contextual information; for example, some of the extracted sentences contained pronouns. These pronouns were impossible to resolve without the information in the preceding sentences. Therefore, extracting the preceding sentence should help in resolving their references. The NLP also has a coreference resolution tool that automatically replaces pronouns with their referenced nouns. However, the tool is in development. We found that it generated too many errors that would require manual checks. Hence, we proceeded without using the tool, and the pronouns identified as agents were ignored. Figure 6: Conceptual model of household food security in Koutila, Mali (EM: Elderly male; YM: Younger male; EF: Elderly female; YF: Younger female). Using our framework, we only collected information on agents, attributes, and actions/interactions. However, ABM also requires information on agent decision-making. Although using social and behavioral theories in defining agent decision-making is predominant, empirically derived decision-making frameworks are context-specific and, therefore, more desirable when ABMs are applied in real-world situations [15; 24]. We realize that some sentences are particularly useful in deriving agent decision-making. Specifically, conditional sentences such as 'if it rains, we plant maize' and compound sentences such as 'when production is low, we buy food from the market' can reveal decision-making. Harvesting these sentences with semantics and machine learning approaches can open new avenues for formulating empirically based decision-making rules for ABM. It is important to note that the derived information is limited by the information contained in the input. For example, we noticed that agent tagging underpredicted agents after using the action and decision verbs. Entities such as 'father' and 'the government' should also be identified as agents of this particular system. However, some information was missed since subjects did not use both types of verbs (action and decision) in the provided interviews. Additionally, stakeholders pointed out that our model structure did not include the dynamics of the governmental and non-governmental actors. It prompts a need for a careful analysis of entities that failed to qualify as agents for data gaps. Furthermore, the interviews went through different translation stages (from local dialects to French and English) that could have corrupted some of their original meanings.