## 4 Results and Discussions
### RQ.2 What Functionalities Are Offered by the Jurisprudence Search Systems?
2 agencies reported that they use recognition of named entities and two agencies use topic modeling, one agency reported that it uses chatbots, 01 agency uses the frequency of teams, as presented in Figure 11.  
Concerning the supervised Machine Learning techniques used for text treatment, 70% of the agencies informed that they use the classification technique, 20% use the regression technique and 10% use the sequential patterns, as presented in Figure 12a. Forty agencies reported that they use the clustering technique as an unsupervised Machine Learning technique. Twenty agencies use sequential patterns. Besides, ten agencies use deviation detection, ten agencies principal component analysis, and ten agencies reported that they use vectorization (Figure 12b). Figure 11: Text mining techniques used in textual database processing system. Thirty-seven agencies reported no extraction technique or template for specific parts of documents in the system they use, such as identification, comments, and conclusion. Only 16 agencies informed that the system they use has some extraction technique or model. Among them were textual search by terms, search by menu and structured abstract, models based on Machine Learning, and models based on Grammars and Characteristics. Forty-four agencies use Natural Language Processing (NLP) technique in the textbase processing system, and nine agencies said yes. Among those mentioned are (a) pre-processing and vectorization of the content of the case records; (b) data collection, raw text extraction, sentence division, tokenization, normalization (systemization, lemmatization), removal of empty words and part-of-speech tagging; (c) part-of-speech tagging, machine learning (classification, clustering, named entity recognition), chunk regular expression, N-gram parsing, feature-based grammars; and (d) tokenization, stop words, stemming, thesaurus, vectors. We also investigated the perception of Brazilian public administration agencies regarding which functionalities a textual base processing system should have. Some of the answers were:  
_\"Systems should offer similar content identification.\"_  
_\"Systems should also perform the search for the content in full, not just the descriptors.\"_  
_\"The systems must allow searching by relevance through advanced filters, such as document type, unit, subject, signatory and dates. In addition they should perform synonym handling and stemming.\"_  
_\"Systems should perform keyword searches, Boolean operators for jurisprudence search, semantic similarity search, cluster analysis and abstract generation.\"_  
Regarding suggestions for improvements to the agency's existing textual base processing system, some responses were:  
_\"Insertion of other databases, identification of a \"paradigm decision\" in the results of the jurisprudence search, identification of citations to decision contents with binding effects in the decisions resulting from the search. Improve response time and use machine learning techniques to improve result ranking.\"_  
_\"Legislative reference and search using fuzzy logic and Artificial Intelligence. In addition, an improvement in document indexing, user interface, and user experience design need to be incorporated.\"_  
Figure 12: (a) shows the supervised Machine Learning techniques used to text processing, while (b) shows the unsupervised Machine Learning techniques. Concerning how the use of Artificial Intelligence, Machine Learning and Text Mining techniques can improve the agency's finalistic activities, some answers were:  
_\"Grouping similar processes and offering a document template to treat each group, offering greater efficiency in audit actions in the selection of objects of greater relevance, risk and materiality. In addition, assisting in the decision making of the subject areas when preparing the annual inspection plan.\"_  
_\"Through the optimization and automation of manual and repetitive work, allowing greater agility in the analysis of processes.