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Article published In: Revista Española de Lingüística Aplicada/Spanish Journal of Applied Linguistics
Vol. 34:2 (2021) ► pp.435–463

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Automatic lexical collocate extraction for corpus-based ontology building and refinement

A FunGramKB case study of the THEFT conceptual scenario

ORCiD logo with link | Universidad Católica San Antonio de Murcia
ORCiD logo with link | University of Granada
Published online: 15 December 2021
DOI logo with link
https://doi.org/10.1075/resla.19030.fer
Abstract
Traditional corpus-based methods rely on manual inspection and extraction of lexical collocates in the study of selection preferences, which is a very costly, labor-intensive, and time-consuming task. Devising automatic methods for lexical collocate extraction becomes necessary to handle this task and the immensity of corpora available. With a view to leveraging the Sketch Engine platform and in-built corpora, we propose a working prototype of a Lexical Collocate Extractor (LeCoExt) command-line tool that mines lexical collocates from all types of verbs according to their syntactic constituents and Collocate Frequency Score (CFS). This might be the first tool that performs comprehensive corpus-based studies of the selection preferences of individual or groups of verbs exploiting the capabilities offered by Sketch Engine. This tool might facilitate the task of extracting rich lexico-semantic knowledge from diverse corpora in a few seconds and at a click away. We test its performance for ontology building and refinement departing from a previous detailed analysis of stealing verbs carried out by Fernández-Martínez, N. J., & Faber, P. (2020). Who stole what from whom? A corpus-based, cross-linguistic study of English and Spanish verbs of stealing. Languages in Contrast, 20(1): 107–140. Google Scholar logo with link to Google Scholar. We show how the proposed tool is used to extract conceptual-cognitive knowledge from the THEFT scenario and implement it into FunGramKB Core Ontology through the creation and modification of theft-related conceptual units.
Resumen
Extracción automática de colocaciones léxicas para la construcción y mejora basadas en corpus de una ontología: estudio de caso del escenario conceptual del robo en FunGramKB
Los métodos tradicionales basados en corpus se valen de la inspección y extracción manual de colocaciones léxicas para el estudio de las preferencias de selección, lo cual es una tarea muy costosa, que requiere mucho trabajo y tiempo. El diseño de métodos automáticos para la extracción de colocaciones léxicas se hace necesario para abordar esta tarea y la inmensidad de los corpus disponibles. Con el fin de aprovechar la plataforma Sketch Engine y sus corpora, proponemos un prototipo funcional de una herramienta de línea de comandos de extracción de colocaciones léxicas, Lexical Collocate Extractor, (LeCoExt) que extrae colocaciones léxicas de todo tipo de verbos según sus constituyentes sintácticos y su puntuación de frecuencia de colocaciones o Collocate Frequency Score (CFS). Esta podría ser la primera herramienta que sirve para realizar estudios exhaustivos basados en corpus sobre las preferencias de selección de verbos individuales o grupos de verbos explotando las capacidades que ofrece Sketch Engine. Esta herramienta podría facilitar la tarea de extraer un rico conocimiento léxico-semántico de diversos corpus en pocos segundos y a un clic de distancia. Probamos su rendimiento para la construcción y mejora de ontologías partiendo de un análisis detallado previo de los verbos de robo en Fernández-Martínez, N. J., & Faber, P. (2020). Who stole what from whom? A corpus-based, cross-linguistic study of English and Spanish verbs of stealing. Languages in Contrast, 20(1): 107–140. Google Scholar logo with link to Google Scholar. Mostramos cómo la herramienta propuesta se utiliza para extraer el conocimiento conceptual-cognitivo del escenario THEFT e implementarlo en la ontología central de FunGramKB a través de la creación y modificación de unidades conceptuales relacionadas con el robo.
Article outline
  • 1.Introduction
  • 2.Theoretical background
    • 2.1Stealing verbs: A lexico-semantic perspective
    • 2.2FunGramKB: Definition, scope and architecture
    • 2.3The FunGramKB ontology
  • 3.Methodology
    • 3.1Presenting the lexical collocate extractor (LeCoExt) tool
    • 3.2FunGramKB conceptual categorization and specifications
  • 4.Results and discussion
    • 4.1Extraction of semantic knowledge
    • 4.2Implementation and refinement of findings into the FunGramKB Core Ontology
    • 4.3Limitations in this contribution
  • 5.Conclusion
  • Notes
  • References
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