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22 - Systemic Functional Linguistics and Computation

New Directions, New Challenges

from Part III - SFL in Application

Published online by Cambridge University Press:  03 May 2019

Geoff Thompson
Affiliation:
University of Liverpool
Wendy L. Bowcher
Affiliation:
Sun Yat-Sen University, China
Lise Fontaine
Affiliation:
Cardiff University
David Schönthal
Affiliation:
Cardiff University
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Summary

Systemic Functional Linguistics has had a long history of interaction with Computational Linguistics. In this chapter we address the current state of the art in attempts to apply systemic-functional linguistic models in computational contexts. The chapter focuses on the computational tasks of parsing, an area that has proved particularly resistant to systemic-functional approaches in the past, dialogue systems, and multimodality. Parsing is an essential prerequisite for many tasks of relevance to the linguist, such as corpus studies. Recent advances in human–machine interaction also emphasize the increasing relevance of dialogue systems, while all interaction and communicative artifacts are nowadays increasingly multimodal. The chapter concludes with a discussion of the value of combining computational and ystemic-functional linguistic accounts for both theory and practice, and sets out some directions for future developments.
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Publisher: Cambridge University Press
Print publication year: 2019

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References

Airoldi, E. M. 2007. Getting Started in Probabilistic Graphical Models. PLoS Computational Biology 3(12): e252.Google Scholar
Arora, S. and Barak, B.. 2009. Computational Complexity: A Modern Approach. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Bateman, J. A. 1997. Enabling Technology for Multilingual Natural Language Generation: The KPML Development Environment. Natural Language Engineering 3(1): 1555.Google Scholar
Bateman, J. A. 2008a. Multimodality and Genre: A Foundation for the Systematic Analysis of Multimodal Documents. Basingstoke: Palgrave Macmillan.Google Scholar
Bateman, J. A. 2008b. Systemic Functional Linguistics and the Notion of Linguistic Structure: Unanswered Questions, New Possibilities. In Webster, J. J., ed., Meaning in Context: Strategies for Implementing Intelligent Applications of Language Studies. Sheffield: Equinox. 2458.Google Scholar
Bateman, J. A. 2014. Text and Image: A Critical Introduction to the Visual/Verbal Divide. London: Routledge.Google Scholar
Bateman, J. A. and O’Donnell, M.. 2015. Computational Linguistics: The Halliday Connection. In Webster, J. J., ed., The Bloomsbury Companion to M. A. K. Halliday. London: Bloomsbury. 453–66.Google Scholar
Bateman, J. A. and Zock, M.. 2017. Natural Language Generation. In Mitkov, R., ed., Oxford Handbook of Computational Linguistics. 2nd ed. Oxford: Oxford University Press. 284304.Google Scholar
Bateman, J. A., Tseng, C., Seizov, O., Jacobs, A., Lüdtke, A., Müller, M. G., and Herzog, O.. 2016. Towards Next-generation Visual Archives: Image, Film and Discourse. Visual Studies 31(2): 131–54.Google Scholar
Bateman, J. A., Wildfeuer, J., and Hiippala, T.. 2017. Multimodality: Foundations, Research and Analysis: A Problem-oriented Introduction. Berlin: Mouton de Gruyter.Google Scholar
Berry, D. M., ed. 2012. Understanding Digital Humanities. Basingstoke: Palgrave Macmillan.Google Scholar
Cao, Y. and O’Halloran, K. L.. 2015. Learning Human Photo Shooting Patterns from Large-Scale Community Photo Collections. Multimedia Tools and Applications 74(24): 11499–516.Google Scholar
Chomsky, N. 1957. Syntactic Structures. The Hague: Mouton and Co.Google Scholar
Collins, M. 2003. Head-driven Statistical Models for Natural Language Parsing. Computational Linguistics 29(4): 589637.Google Scholar
Costetchi, E. 2013. A Method to Generate Simplified Systemic Functional Parses from Dependency Parses. In Proceedings of the Second International Conference on Dependency Linguistics. Prague, August 27–30, 2013. Charles University in Prague. Prague: Matfyzpress. 6877.Google Scholar
Couto-Vale, D. 2017. How to Make a Wheelchair Understand Spoken Commands. PhD Thesis, Bremen University.Google Scholar
Doermann, D. and Tombre, K., eds. 2014. Handbook of Document Image Processing and Recognition. London: Springer-Verlag.Google Scholar
Domingos, P., Kok, S., Lowd, D., and Poon, H.. 2010. Markov Logic. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology 17(11): 1491–508.Google Scholar
Fawcett, R. P. 1988. Language Generation as Choice in Social Interaction. In Zoch, M. and Sabah, G., eds., Advances in Natural Language Generation. London: Pinter. 2749.Google Scholar
Fawcett, R. P. 1989. Towards a Systemic Flowchart Model for Discourse Analysis. In Fawcett, R. P. and Young, D., eds., New Developments in Systemic Linguistics: Theory and Application. London: Pinter. 116–43.Google Scholar
Fawcett, R. P. 1993. The Architecture of the COMMUNAL Project in NLG (and NLU). In The Fourth European Workshop on Natural Language Generation. Pisa.Google Scholar
Fawcett, R. P. 2008. Invitation to Systemic Functional Linguistics through the Cardiff Grammar. Sheffield: Equinox.Google Scholar
Fischer, K. 2016. Designing Speech for a Recipient: The Roles of Partner Modeling, Alignment and Feedback in So-called ‘Simplified Registers’. Amsterdam: John Benjamins.Google Scholar
Grinstead, C. M. and Snell, J. L.. 2012. Introduction to Probability. Providence: American Mathematical Society.Google Scholar
Guo, H. and Hsu, W.. 2002. A Survey of Algorithms for Real-time Bayesian Network Inference. In The Joint AAAI/KDD/UAI02 Workshop on Real-time Decision Support and Diagnosis Systems. Edmonton, Canada.Google Scholar
Guo, L. 2004. Multimodality in a Biology Textbook. In O’Halloran, K. L., ed., Multimodal Discourse Analysis: Systemic Functional Perspectives. London: Continuum. 196219.Google Scholar
Haegeman, L. 1991. Introduction to Government and Binding Theory, Volume 2. London: Blackwell.Google Scholar
Halliday, M. A. K. 1996. On Grammar and Grammatics. In Hasan, R., Cloran, C., and Butt, D., eds., Functional Descriptions: Theory in Practice. Amsterdam: John Benjamins. 138.Google Scholar
Halliday, M. A. K. and Matthiessen, C. M. I. M.. 2014. Halliday’s Introduction to Functional Grammar. 4th ed. Oxford: Routledge.Google Scholar
Hasan, R. 1987. The Grammarian’s Dream: Lexis as Most Delicate Grammar. In Halliday, M. A. K. and Fawcett, R. P., eds., New Developments in Systemic Linguistics, Volume 1. London: Frances Pinter. 184211.Google Scholar
Hiippala, T. 2016. Semi-automated Annotation of Page-based Documents within the Genre and Multimodality Framework. In Proceedings of the 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. Berlin: Association for Computational Linguistics. 84–9.Google Scholar
Honnibal, M. 2004. Converting the Penn Treebank to Systemic Functional Grammar. In Proceedings of the Australasian Language Technology Workshop (ALTW04). Available online at: www.alta.asn.au/events/altw2004/publication/04-27.pdf. (Last accessed 12/09/2017.)Google Scholar
Honnibal, M. and Curran, J. R.. 2007. Creating a Systemic Functional Grammar Corpus from the Penn Treebank. In Proceedings of the ACL 2007 Workshop on Deep Linguistic Processing. Prague: Association for Computational Linguistics. 8996.Google Scholar
Kasper, R. 1988. An Experimental Parser for Systemic Grammars. In Proceedings of the 12th International Conference on Computational Linguistics. Budapest: John von Neumann Society for Computing. 309–12.Google Scholar
Kembhavi, A., Salvato, M., Kolve, E., Seo, M., Hajishirzi, H., and Farhadi, A.. 2016. A Diagram Is Worth a Dozen Images. In Proceedings of the 14th European Conference in Computer Vision. Amsterdam, The Netherlands, October 11–14, 2016. Cham: Springer. 235–51.Google Scholar
Kersting, K. and De Raedt, L.. 2007. Bayesian Logic Programming: Theory and Tool. In Getoor, L. and Taskar, B., eds., Introduction to Statistical Relational Learning. Cambridge, MA: MIT Press. 291321.CrossRefGoogle Scholar
Kotthoff, L. 2014. Algorithm Selection for Combinatorial Search Problems: A Survey. AI Magazine 35(3): 4860.Google Scholar
Kress, G. and van Leeuwen, T.. 2006. Reading Images: The Grammar of Visual Design. 2nd ed. London: Routledge.Google Scholar
LeCun, Y., Bengio, Y., and Hinton, G.. 2015. Deep Learning. Nature 521: 436–44.Google Scholar
Mann, W. C. 1983. An Overview of the PENMAN Text Generation System. In Proceedings of the Third National Conference on Artificial Intelligence. Menlo Park: The AAAI Press. 261–5.Google Scholar
Mann, W. C. and Matthiessen, C. M. I. M.. 1985. Demonstration of the Nigel Text Generation Computer Program. In Benson, J. D. and Greaves, W. S., eds., Systemic Perspectives on Discourse, Volume 1. Norwood: Ablex. 5083.Google Scholar
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., and McClosky, D.. 2014. The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore: Association for Computational Linguistics. 5560.Google Scholar
Marneffe, M., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J., and Manning, C. D.. 2014. Universal Stanford Dependencies: A Cross-linguistic Typology. In Proceedings of the 9th International Conference on Language Resources and Evaluation. Reykjavik: European Language Resources Association (ELRA). 4585–92.Google Scholar
Marneffe, M., MacCartney, B., and Manning, C. D.. 2006. Generating Typed Dependency Parses from Phrase Structure Parses. Lrec 6(3): 449–54.Google Scholar
Matthiessen, C. M. I. M. 1985. The Systemic Framework in Text Generation: Nigel. In Benson, J. and Greaves, W., eds., Systemic Perspective on Discourse, Voume 1. Norwood: Ablex. 96118.Google Scholar
Matthiessen, C. M. I. M. 2015a. Halliday’s Conception of Language as a Probabilistic System. In Webster, J. J., ed., The Bloomsbury Companion to M. A. K. Halliday. London: Bloomsbury. 203–41.Google Scholar
Matthiessen, C. M. I. M. 2015b. Register in the Round: Registerial Cartography. Functional Linguistics 2(9): 148.CrossRefGoogle Scholar
McDonald, D. 1980. Natural Language Production as a Process of Decision Making under Constraint: Cambridge, MA: MIT Press.Google Scholar
McDonald, D. and Woodward-Kron, R.. 2016. Member Roles and Identities in Online Support Groups: Perspectives from Corpus and Systemic Functional Linguistics. Discourse and Communication 10(2): 157–75.Google Scholar
Neale, A. C. 2002. More Delicate TRANSITIVITY: Extending the PROCESS TYPE for English to Include Full Semantic Classifications. PhD Thesis, Cardiff University.Google Scholar
Nivre, J. 2015. Towards a Universal Grammar for Natural Language Processing. In Gelbukh, A., ed., Computational Linguistics and Intelligent Text Processing. Berlin: Springer. 316.Google Scholar
O’Donnell, M. 1990. A Dynamic Model of Exchange. Word 41(3): 293328.Google Scholar
O’Donnell, M. 1993. Reducing Complexity in a Systemic Parser. In Proceedings of the Third International Workshop on Parsing Technologies. Tilburg: Association for Computational Linguistics. 203–17.Google Scholar
O’Donnell, M. 1994. Sentence Analysis and Generation: A Systemic Perspective. PhD Thesis, University of Sydney.Google Scholar
O’Donnell, M. 2005. The UAM Systemic Parser. In Proceedings of the 1st Computational Systemic Functional Grammar Conference. Sydney: University of Sydney. 4755.Google Scholar
O’Donnell, M. 2008. Demonstration of the UAM Corpus Tool for Text and Image Annotation. In Proceedings of the ACL ’08: HLT Demo Session. Columbus: Association for Computational Linguistics. 1316.Google Scholar
O’Donnell, M. and Bateman, J. A.. 2005. SFL in Computational Contexts: A Contemporary History. In Hasan, R., Matthiessen, C. M. I. M., and Webster, J. J., eds., Continuing Discourse on Language: A Functional Perspective, Volume 1. Sheffield: Equinox. 343–82.Google Scholar
Oepen, S., Flickinger, D., Uszkoreit, H., and Tsujii, J.. 2000. Introduction to the Special Issue on Efficient Processing with HPSG. Natural Language Engineering 6(1): 114.CrossRefGoogle Scholar
O’Halloran, K. L. 2008. Systemic Functional-Multimodal Discourse Analysis (SF-MDA): Constructing Ideational Meaning Using Language and Visual Imagery. Visual Communication 7(4): 443–75.Google Scholar
O’Halloran, K. L. 2014. Multimodal Discourse Analysis. In Hyland, K. and Paltridge, B., eds., The Bloomsbury Companion to Discourse Analysis. London: Bloomsbury. 120–37.Google Scholar
O’Halloran, K. L. 2015. Multimodal Digital Humanities. In Trifonas, P. P., ed., International Handbook of Semiotics. Dordrecht: Springer. 389416.Google Scholar
O’Halloran, K. L., Chua, A., and Podlasov, A.. 2014a. The Role of Images in Social Media Analytics: A Multimodal Digital Humanities Approach. In Machin, D., ed., Visual Communication. Berlin: Mouton de Gruyter. 565–88.Google Scholar
O’Halloran, K. L., Kwan Lin, M. E., and Tan, S.. 2014b. Multimodal Analytics: Software and Visualization Techniques for Analyzing and Interpreting Multimodal Data. In Jewitt, C., ed., The Routledge Handbook of Multimodal Analysis. 2nd ed. London: Routledge. 386–96.Google Scholar
O’Halloran, K. L., Tan, S., Wignell, P., Bateman, J. A., Pham, D., Grossman, M., and Vande Moere, A.. 2016. Interpreting Text and Image Relations in Violent Extremist Discourse: A Mixed Methods Approach for Big Data Analytics. Terrorism and Political Violence. DOI: 10.1080/09546553.2016.1233871CrossRefGoogle Scholar
O’Halloran, K. L., Tan, S., Pham, D., Bateman, J. A., and Vande Moere, A.. 2018. A Digital Mixed Methods Research Design: Integrating Multimodal Analysis with Data Mining and Information Visualization for Big Data Analytics. Journal of Mixed Methods Research 12(1): 1130.Google Scholar
O’Toole, M. 2011. The Language of Displayed Art. 2nd ed. Abingdon: Routledge.Google Scholar
Peters, S. P. and Ritchie, R. W.. 1973. On the Generative Power of Transformational Grammars. Information Sciences 6: 4983.Google Scholar
Rautaray, S. S. and Agrawal, A.. 2015. Vision Based Hand Gesture Recognition for Human Computer Interaction: A Survey. Artificial Intelligence Review 43(1): 154.Google Scholar
Richardson, M. and Domingos, P.. 2006. Markov Logic Networks. Machine Learning 62(1–2): 107–36.Google Scholar
Schreibman, S., Siemens, R., and Unsworth, J., eds. 2016. A New Companion to Digital Humanities. 2nd ed. Chichester: Wiley-Blackwell.Google Scholar
Shi, H., Jian, C., and Rachuy, C.. 2011. Evaluation of a Unified Dialogue Model for Human-Computer Interaction. International Journal of Computational Linguistics and Applications 2(1): 155–73.Google Scholar
Sleator, D. D. K. and Temperley, D.. 1993. Parsing English with a Link Grammar. In Third International Workshop on Parsing Technologies (IWPT). Tillburg: Association for Computational Linguistics. 277–92. Available online at: www.link.cs.cmu.edu/link/ftp-site/link-grammar/LG-IWPT93.pdf. (Last accessed 12/09/2017.)Google Scholar
Socher, R., Bauer, J., Manning, C. D., and Ng, A. Y.. 2013. Parsing with Compositional Vector Grammars. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics. 455–65.Google Scholar
Steedman, M. J. 1993. Categorial Grammar. Lingua 90: 221–58.Google Scholar
Steels, L. 2005. The Emergence and Evolution of Linguistic Structure: From Lexical to Grammatical Communication Systems. Connection Science 17(3–4): 213–30.CrossRefGoogle Scholar
Svensson, P. 2010. The Landscape of Digital Humanities. Digital Humanities Quarterly 4(1). Available online at: www.digitalhumanities.org/dhq/vol/4/1/000080/000080.html. (Last accessed 12/09/2017.)Google Scholar
Teich, E., Degaetano-Ortlieb, S., Fankhauser, P., Kermes, H., and Lapshinova-Koltunski, E.. 2016. The Linguistic Construal of Disciplinarity: A Data Mining Approach Using Register Features. Journal of the Association for Information Science and Technology (JASIST) 67(7): 1668–78.Google Scholar
Teich, E., Hagen, E., Grote, B., and Bateman, J. A.. 1997. From Communicative Context to Speech: Integrating Dialogue Processing, Speech Production and Natural Language Generation. Speech Communication 21(1–2): 7399.CrossRefGoogle Scholar
Traum, D. and Larsson, S.. 2003. The Information State Approach to Dialogue Management. In Smith, R. and van Kuppevelt, J., eds., Current and New Directions in Discourse and Dialogue. Dordrecht: Kluwer Academic Publishers. 325–53.Google Scholar
Tseng, C. 2013. Cohesion in Film: Tracking Film Elements. Basingstoke: Palgrave Macmillan.Google Scholar
Weerasinghe, R. 1994. Probabilistic Parsing in Systemic Functional Grammar. PhD Thesis, School of Computing Mathematics, University of Wales College of Cardiff.Google Scholar
Winograd, T. 1972. Understanding Natural Language. New York: Academic Press.Google Scholar
XTAG Research Group. 2001. A Lexicalized Tree Adjoining Grammar for English. IRCS, University of Pennsylvania. Available online at: www.cis.upenn.edu/~xtag/tech-report/tech-report.html. (Last accessed 12/09/2017.)Google Scholar
Zinn, J. O. and McDonald, D.. 2015. Changing Discourses of Risk and Health Risk. In Chamberlain, J. M., ed., Medicine, Risk, Discourse and Power. London: Routledge. 207–40.Google Scholar

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