TY - GEN
T1 - Text Analysis Software Using Topic Modeling Techniques for the Extraction of Knowledge from Cases Related to Vulnerability and Access to Justice
AU - Espinosa, Jorge E.
AU - Mateus, Sandra P.
AU - Ramirez, Diana M.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Access to justice is a vital part of the UNDP (United Nations Development Programme) mandate to reduce poverty and strengthen democratic governance. One of the objectives of the Personero (ombudsmen) in emerging countries is to assist people in accessing justice, promoting equality and the protection of the most vulnerable, providing assistance to citizens through guidance or consultations. In Medellín (Colombia), the “Personería de Medellín” (ombudsman’s office) has established a citizen service system staffed by lawyers who address their difficulties, needs, or other issues, guiding them on the steps to take, such as filing legal actions or providing advice. All this information is recorded in a digitalized system. Annually, more than 16,000 different cases can be recorded (e.g. for year 2019), and despite the implementation of multidimensional analysis tools like dashboards, there is a need to implement Natural Language Processing (NLP) techniques to semantically analyze the fields of unstructured text information. This paper presents an application offered to citizens called Person_IA, which implements various functionalities based on artificial intelligence techniques, particularly Natural Language Processing (NLP). This allows for text analysis using strategies such as topic modeling, word clouds, n-grams, among others, to extract knowledge from the unstructured information in the citizen service system. This helps generate indicator reports and analyze variables that enable efficient decision-making by knowledge managers at the Medellín Ombudsman’s Office, facilitating access to justice for the vulnerable population in this city.
AB - Access to justice is a vital part of the UNDP (United Nations Development Programme) mandate to reduce poverty and strengthen democratic governance. One of the objectives of the Personero (ombudsmen) in emerging countries is to assist people in accessing justice, promoting equality and the protection of the most vulnerable, providing assistance to citizens through guidance or consultations. In Medellín (Colombia), the “Personería de Medellín” (ombudsman’s office) has established a citizen service system staffed by lawyers who address their difficulties, needs, or other issues, guiding them on the steps to take, such as filing legal actions or providing advice. All this information is recorded in a digitalized system. Annually, more than 16,000 different cases can be recorded (e.g. for year 2019), and despite the implementation of multidimensional analysis tools like dashboards, there is a need to implement Natural Language Processing (NLP) techniques to semantically analyze the fields of unstructured text information. This paper presents an application offered to citizens called Person_IA, which implements various functionalities based on artificial intelligence techniques, particularly Natural Language Processing (NLP). This allows for text analysis using strategies such as topic modeling, word clouds, n-grams, among others, to extract knowledge from the unstructured information in the citizen service system. This helps generate indicator reports and analyze variables that enable efficient decision-making by knowledge managers at the Medellín Ombudsman’s Office, facilitating access to justice for the vulnerable population in this city.
KW - Latent Dirichlet Assignment (LDA)
KW - Latent Semantic Analysis (LSA)
KW - Machine Learning
KW - Natural Language Processing (NLP)
KW - Textual Analysis
KW - Topic Modelling
UR - http://www.scopus.com/inward/record.url?scp=85195522318&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-60615-1_23
DO - 10.1007/978-3-031-60615-1_23
M3 - Contribución a la conferencia
AN - SCOPUS:85195522318
SN - 9783031606144
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 334
EP - 352
BT - Artificial Intelligence in HCI - 5th International Conference, AI-HCI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Proceedings
A2 - Degen, Helmut
A2 - Ntoa, Stavroula
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Artificial Intelligence in HCI, AI-HCI 2024, held as part of the 26th International Conference on Human-Computer Interaction, HCI International 2024
Y2 - 29 June 2024 through 4 July 2024
ER -