Evaluating Information Extraction Approaches in the Construction of a Real Estate Observatory

Página de ítem simple
cic.institucionOrigenLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.isFulltextSI
cic.isPeerReviewedSI
cic.lugarDesarrolloLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.parentTypeObjeto de conferencia
cic.versionAceptada
dc.date.accessioned2025-09-05T12:12:42Z
dc.date.available2025-09-05T12:12:42Z
dc.identifier.urihttps://digital.cic.gba.gob.ar/handle/11746/12550
dc.titleEvaluating Information Extraction Approaches in the Construction of a Real Estate Observatoryen
dc.typeDocumento de conferencia
dcterms.abstractA real estate observatory plays a significant role in the aggregation and analysis of real estate market data. The information that lies in real estate advertisements can be leveraged to populate such an observatory. However, this data can present itself in both a structured and an unstructured manner. Unstructured data represents a problem to automatically process and extract information since it lacks a predefined structure. Thus, there’s a need for techniques to give structure to unstructured data. Information Extraction (IE) is the process of structuring data from unstructured data. Natural Language Processing techniques enable machines to understand texts, making them particularly significant in the context of IE. This work evaluates both rule-based and machine-learning based IE approaches to extract features from real estate descriptions within advertisements. Those features are relevant in the context of real estate observatory construction. The performance of each approach is measured using precision, recall and f1-score metrics.en
dcterms.creator.authorTanevitch, Luciana
dcterms.creator.authorAntonelli, Leandro
dcterms.creator.authorTorres, Diego
dcterms.identifier.otherISBN: 978-3-031-91690-8
dcterms.identifier.otherDOI: 10.1007/978-3-031-91690-8_6
dcterms.isPartOf.itemCollaboration in Knowledge Discovery and Decision Making. DECISIONING 2024
dcterms.isPartOf.seriesDecisioning 2024 (Colombia, 4 al 6 de junio de 2024)
dcterms.issued2025
dcterms.languageInglés
dcterms.licenseAttribution-NonCommercial-NoDerivatives 4.0 International (BY-NC-ND 4.0)
dcterms.subjectInformation Extractionen
dcterms.subjectNatural Language Processingen
dcterms.subjectReal Estate Observatoryen
dcterms.subject.materiaCiencias de la Computación e Información

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
Evaluating Information Extraction.pdf-PDFA.pdf
Tamaño:
275.21 KB
Formato:
Adobe Portable Document Format
Descripción:
Documento completo
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
Miniatura
Nombre:
license.txt
Tamaño:
3.46 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Artículos y presentaciones en Congresos