Ulysses is a comprehensive set of machine learning modules tailored specifically for Brazil's Chamber of Deputies. Our project aims providing tools for data analysis and decision-making processes within the legislative domain of Brazil.

• O. ALBUQUERQUE, Hidelberg et al. UlyssesNERQ: Expanding Queries from Brazilian Portuguese Legislative Documents through Named Entity Recognition. 16th International Conference on Computational Processing of Portuguese (PROPOR 2024). Qualis A4

• PRESSATO, Diany et al. (2024) Natural Language Processing Application in Legislative Activity: a Case Study of Similar Amendments in the Brazilian Senate. 16th International Conference on Computational Processing of Portuguese (PROPOR 2024). Qualis A4

• MAIA, Dyonatan. F.; et al. (2024) Enhancing Stance Detection in Low-Resource Brazilian Portuguese Using Corpus Expansion generated by GPT-3.5. 16th International Conference on Computational Processing of Portuguese (PROPOR 2024). Qualis A4

• GARCIA, Eduardo; et al. (2024) RoBERTaLexPT: A Legal RoBERTa Model pretrained with deduplication for Portuguese. 16th International Conference on Computational Processing of Portuguese (PROPOR 2024). Qualis A4

• ALBUQUERQUE, Hidelberg et al (2023). Named Entity Recognition: a Survey for the Portuguese Language. Procesamiento del Lenguaje Natural, [S.l.], v. 70, p. 171-185, mar. 2023. ISSN 1989-7553. Disponível em: http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6488. (Qualis A1) https://doi.org/10.26342/2023-70-14

• JÚNIOR. Flávio Rocha et al. (2023) Avaliação de frameworks para Recuperação de Documentos Legislativos: um Estudo de Caso na Câmara dos Deputados Brasileira. WCGE - XI Workshop de Computação Aplicada em Governo Eletrônico - CSBC 2023. (Qualis B4) https://doi.org/10.5753/wcge.2023.229925

• O. ALBUQUERQUE, Hidelberg et al. (2023) On the Assessment of Deep Learning Models for Named Entity Recognition of Brazilian Legal Documents. 22nd Conference on Artificial Intelligence - EPIA2023. (Qualis B2) https://doi.org/10.1007/978-3-031-49011-8_8

• ALBUQUERQUE, Hidelberg. O. et al. (2022) UlyssesNER-Br: a Corpus of Brazilian Legislative Documents for Named Entity. In: 15th International Conference on Computational Processing of Portuguese (PROPOR 2022). Lecture Notes in Computer Science, vol 13208. Springer, Cham. https://doi.org/10.1007/978-3-030-98305-5_1. (Qualis A4)

• COSTA, Rosimeire. P. et al. (2022) . Expanding UlyssesNER-Br Named Entity Recognition Corpus with Informal User-generated Text. In: European Conference on Artificial Intelligence (EPIA 2022). Proceedings of the EPIA. (Qualis B2)

• MAIA, Dyonatan. F.; et al. (2022). UlyssesSD-Br: Stance Detection in Brazilian Political Polls. In: European Conference on Artificial Intelligence (EPIA 2022). Proceedings of the EPIA. (Qualis B2)

• VITÓRIO, Douglas et al. (2022) Ulysses-RFSQ: a novel method to improve Legal Information Retrieval based on Relevance Feedback. In: 11th Brazilian Conference on Intelligent Systems (BRACIS 2022). Proceedings of the BRACIS. (Qualis A4)

• COSTA, Marília et. al. No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling. Preprint. https://www.researchgate.net/publication/362467723_No_Pattern_No_Recognition_a_Survey_about_Reproducibility_and_Distortion_Issues_of_Text_Clustering_and_Topic_Modeling/stats (sem Qualis)

• SILVA, Nádia.F.F.. et al. (2021) Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies. In: Britto A., Valdivia Delgado K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science, vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_8. (Qualis A4)

• SOUZA, Ellen. et al. (2021) Assessing the Impact of Stemming Algorithms Applied to Brazilian Legislative Documents Retrieval. In: XIII Brazilian Symposium in Information and Human Language Technology (STIL 2021). DOI: https://doi.org/10.5753/stil.2021.17802. (Qualis B1)

• SOUZA, Ellen. et al. (2021) An Information Retrieval Pipeline for Legislative Documents from the Brazilian Chamber of Deputies. In: 34th International Conference on Legal Knowledge and Information Systems (JURIX 2021). https:/DOI:10.3233/FAIA210326. (Qualis B1)

flowchart LR

package_optimizer("Ulysses Optimizer")
package_curiosity("Ulysses Curiosity")
package_segmenter("Ulysses Segmenter")
package_fetcher("Ulysses Fetcher")

subgraph microservice_comparer["Ulysses Document Comparer"]
    direction TB
    microservice_expandQuery["expand-query"]
    microservice_lookForReferenced["look-for-referenced"]
    microservice_lookForSimilar["look-for-similar"]
    microservice_saveRelevanceFeedback["save-relevance-feedback"]

    microservice_expandQuery --- microservice_lookForReferenced --- microservice_lookForSimilar --- microservice_saveRelevanceFeedback

    linkStyle 0 stroke-width:0px;
    linkStyle 1 stroke-width:0px;
    linkStyle 2 stroke-width:0px;
end

subgraph microservice_analyzer["Ulysses Argumentation Analyzer"]
    direction TB
    microservice_clusterComments["clusterComments"]
    microservice_mapToDocument["mapToDocument (map2doc)"]

    microservice_clusterComments --- microservice_mapToDocument

    linkStyle 3 stroke-width:0px;
end

package_segmenter --> microservice_analyzer
package_fetcher   --> microservice_analyzer
package_optimizer --> microservice_analyzer
package_fetcher   --> package_segmenter
package_fetcher   --> package_curiosity

microservice_analyzer --- microservice_comparer
linkStyle 9 stroke-width:0px;

classDef default fill:#333333,color:white,stroke-width:2px,stroke:#AAAAAA;
classDef clsBaseModule fill:#4D644D;
classDef clsIntegrationModule fill:#644D51,font-size:13px,color:white;
classDef clsMicroservice fill:#514D64,font-size:16px;

class package_optimizer,package_curiosity,package_segmenter,package_fetcher clsBaseModule;
class microservice_analyzer,microservice_comparer clsIntegrationModule;
class microservice_clusterComments,microservice_mapToDocument clsMicroservice;
class microservice_expandQuery,microservice_lookForReferenced,microservice_lookForSimilar,microservice_saveRelevanceFeedback clsMicroservice;

1. Base modules:
   • Ulysses Fetcher: fetch pretrained models stored in cloud services;
   • Ulysses Optimizer: quantization and optimization methods pretrained model;
   • Ulysses Segmenter: semantic segmentation of legal documents into legal items;
   • Ulysses Curiosity: probe and validate pretrained models;
2. Integration modules:
   • Ulysses Argumentation Analyzer:
     • (microservice) clusterComments;
     • (microservice) mapToDocument.
   • Ulysses Document Comparer:
     • (microservice) look-for-similar;
     • (microservice) look-for-referenced;
     • (microservice) expand-query;
     • (microservice) save-relevance-feedback.

Additional research code meant for scientific publication is available at Ulysses (publicações).