A Modelling Language for Discourse Analysis in HumanitiesDefinition, Design, Validation and First Experiences
- Patricia Martin Rodilla 1
- César González-Pérez 1
- 1 Instituto de Ciencias del Patrimonio. Consejo Superior de Investigaciones Científicas
ISSN: 2531-1786
Año de publicación: 2017
Número: 1
Páginas: 368-378
Tipo: Artículo
Otras publicaciones en: Revista de Humanidades Digitales
Objetivos de desarrollo sostenible
Resumen
Due to humanities generally produce knowledge in textual formats (e.g. narrative conclusions or reports), a properly management of the humanities corpus needs methods for conceptualizing and extracting information from textual sources. Discourse analysis techniques allow extracting information in terms of the connection between discourse structure and elements of the reality referred in the text, as well as the inferential dimension. This semantic information is not available following other extraction methods from texts. In order to formalize the discourse analysis application for textual sources in humanities, a modelling language has been defined and initially validated with humanities specialists, showing the discourse structure and the semantic and inferential aspects extracted.
Referencias bibliográficas
- AGRAWAL, R., IMIELINSKI, T. and SWAMI, A. (1993). “Mining Association Rules between Sets of Items in Large Databases Proceedings of the ACM SIGMOD Conference (Washington, May 1993), 22.2, 207-216.
- BAEZA-YATES, R. and RIBEIRO-NETO, B. (1999). Modern Information Retrieval. New York: ACM Press.
- BORTH, D., JI, R., CHEN, T., BREUEL, T. and CHANG, S.-F. (2013). “Large-scale Visual Sentiment Ontology and Detectors Using Adjective Noun Pairs”. Proceedings of the 21st ACM International Conference on Multimedia
- HOBBS, J.R. (1985). On the Coherence and Structure of Discourse. Stanford: Stanford University, Center for the Study of Language and Information.
- ISO/IEC (2006). Topics Maps. ISO/IEC 13250/2006.
- JENSEN, L.J., SARIC, J. and BORK, P. (2006). “Literature Mining for the Biologist: from Information Retrieval to Biological Discovery”. Nature Reviews Genetics, 7.2, 119-129.
- LE BRUN, A. and PELTENBURG, E. (2004). “The Colonization and Settlement of Cyprus. Investigations at Kissonerga-Mylouthkia, 1976-1996”. Paléorient, 30.1, 194-196.
- MARTÍN-RODILLA, P. (2013). “Software-Assisted Knowledge Generation in the Archaeological Domain: A Conceptual Framework”. Proceedings of the Doctoral Consortium of the 25th International Conference on Advanced Information Systems Engineering (CAiSE) (Valencia, June 21, 2013).
- MARTÍN-RODILLA, P. and GONZÁLEZ-PÉREZ, C. (2014). An ISO/IEC 24744-Derived Modelling Language for Discourse Analysis. Oral presentation at: Research Challenges in Information Science (RCIS), 2014 IEEE Eighth International Conference.
- Mc KEVITT, P., PARTRIDGE, D. and WILKS, Y. (1999). “Why Machines Should Analyse Intention in Natural Language Dialogue”. International Journal of Human-Computer Studies, 51.5, 947-989.
- PANG, B., LEE, L. and VAITHYANATHAN, S. (2002). “Thumbs up?: Sentiment Classification Using Machine Learning Techniques”. Proceedings of the ACL Conference on Empirical Methods in Natural Language Processing (EMNLP), 10 (Philadelphia, July 2002), 79-86. Association for Computational Linguistics.
- PELTENBURG, E. (2009). Kissonerga-Mylouthkia, Cyprus 1976-1996.
- POLANYI, L. (1988). “A Formal Model of the Structure of Discourse.” Journal of Pragmatics, 12.5, 601-638.
- SOMEREN, M.V., BARNARD, Y.F. and SANDBERG, J.A. (1994). The Think Aloud Method: A Practical Approach to Modelling Cognitive Processes. San Diego: San Diego State University, Department of Educational Technology, Academic Press.
- TORRES-MORENO, J.-M. (2010). “Reagrupamiento en familias y lexematización automática independientes del idioma.” Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial,14.47, 38-53.
- WHITE, S.A. (2004). Introduction to BPMN