In addition, to collect the public agencies perception of their Jurisprudence Search system and the advantages and challenges of the used systems. Therefore, the goal of this study is to identify which Brazilian agencies use a Jurisprudence search system and to map if they developed it by themselves or the usage of third-party software. No agency participating in the survey claimed to use ontology to treat structured and unstructured data from different sources and formats.ĭue to the diversity of the existing systems in this scenario, it is essential to identify the existing Jurisprudence systems and what technological solutions they use to perform document classification and selection. On the other hand, the systems do not use too many artificial intelligence and morphological construction techniques. Around 87% of the jurisprudence search systems use machine learning classification. Our findings revealed that the prevailing technologies of Brazilian agencies in developing jurisdictional search systems are Java programming language and Apache Solr as the main indexing engine. We conducted a literature review and a survey to investigate the characteristics and functionalities of the jurisprudence search systems used by Brazilian public administration agencies. This paper presents a proposed solution architecture for the jurisprudence search system of the Brazilian Administrative Council for Economic Defense (CADE), with a view to building and expanding the knowledge generated regarding the economic defense of competition to support the agency’s final procedural business activities. In the similarity of legal decisions, jurisprudence seeks subsidies that provide stability, uniformity, and some predictability in the analysis of a case decided. Questions explained agreeable preferred strangers too him her son.A jurisprudence search system is a solution that makes available to its users a set of decisions made by public bodies on the recurring understanding as a way of understanding the law. Highlighter = new UnifiedHighlighter(searcher, analyzer) įragments = highlighter.highlight("contents", query, hits) Query = new WildcardQuery(new Term("contents", "prefer?d")) String fragments = highlighter.highlight("contents", query, hits) UnifiedHighlighter highlighter = new UnifiedHighlighter(searcher, analyzer) ("Search terms found in :: " + hits.totalHits + " files") TopDocs hits = arch(query, 10, Sort.INDEXORDER) Query query = new WildcardQuery(new Term("contents", "prefer*")) IndexSearcher searcher = new IndexSearcher(reader) Īnalyzer analyzer = new StandardAnalyzer() IndexReader reader = DirectoryReader.open(dir) Index reader - an interface for accessing a point-in-time view of a lucene index Public static void main(String args) throws Exceptionĭirectory dir = FSDirectory.open(Paths.get(INDEX_DIR)) Private static final String INDEX_DIR = "indexedFiles" This contains the lucene indexed documents If you want to learn more about creating lucene indexes with text files, follow linked article. In this example, I am reusing the indexes created in previous lucene example.
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