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28 - Natural Language Understanding and Generation

from Part IV - Computational Modeling in Various Cognitive Fields

Published online by Cambridge University Press:  21 April 2023

By
Marjorie McShane  and
Sergei Nirenburg
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

In the framework of computational cognitive modeling, natural language understanding and generation must be integrated with other cognitive capabilities, such as reasoning and learning. The language understanding component of an intelligent agent extracts and formally represents the meaning of texts and dialog turns. The output of language understanding must reflect the speaker’s intended meaning and be sufficiently detailed to serve as input to reasoning and action in artificial intelligent agents. One kind of agent action is verbal, so agents must include a language generation capability. This chapter describes a particular language understanding system that meets the requirements for the above language capabilities and also puts forward methodological arguments about the interplay between theories, models, and computational systems.

Keywords

natural language understandingnatural language generationlanguage-endowed intelligent agentssemanticspragmaticsdiscoursetheoriesmodels and systems
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 921 - 946
Publisher: Cambridge University Press
Print publication year: 2023

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References

Bickerton, D. (1990). Language and Species. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Bratman, M. E. (1987). Intentions, Plans, and Practical Reason. Cambridge, MA: Harvard University Press.Google Scholar
Cantrell, R., Schermerhorn, P., & Scheutz, M. (2011). Learning actions from human-robot dialogues. In Proceedings of the 2011 IEEE Symposium on Robot and Human Interactive Communication (pp. 125–130). IEEE Press.Google Scholar
Church, K. (2011). A pendulum swung too far. Linguistic Issues in Language Technology, 6 , 1–27.Google Scholar
Culicover, P. W., & Jackendoff, R. (2005). Simpler Syntax. Oxford: Oxford University Press.Google Scholar
Demberg, V., Keller, F., & Koller, A. (2013). Incremental, predictive parsing with psycholinguistically motivated tree-adjoining grammar. Computational Linguistics, 39 (4), 1025–1066.CrossRefGoogle Scholar
English, J., & Nirenburg, S. (2020). OntoAgent: implementing content-centric cognitive models. In Proceedings of the 2020 Conference on Advances in Cognitive Systems.Google Scholar
Fillmore, C. J., & Baker, C. F. (2012). A frames approach to semantic analysis. In Heine, B. & Narrog, H. (Eds.),The Oxford Handbook of Linguistic Analysis (Chapter 13, pp. 313–340). Oxford: Oxford University Press.CrossRefGoogle Scholar
Fox, J. J. (1977). Roman Jakobson and the comparative study of parallelism. In van Schooneveld, C. H. & Armstrong, D. (Eds.), Roman Jakobson: Echoes of His Scholarship (pp. 59–90). The Hague: Peter de Ridder Press.Google Scholar
Goodall, G. (1987). Parallel Structures in Syntax: Coordination, Causatives and Restructuring. Cambridge: Cambridge University Press.Google Scholar
Hobbs, J., & Kehler, A. (1997). A theory of parallelism and the case of VP ellipsis. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics (ACL-98) (pp. 394–401).CrossRefGoogle Scholar
Hoffman, T., & Trousdale, G. (Eds.) (2013). The Oxford Handbook of Construction Grammar. Oxford: Oxford University Press.Google Scholar
Jackendoff, R. (2002). Foundations of Language: Brain, Meaning, Grammar, Evolution. Oxford: Oxford University Press.CrossRefGoogle Scholar
Jackendoff, R., & Wittenberg, E. (2014). What you can say without syntax: a hierarchy of grammatical complexity. In Newmeyer, F. & Preston, L. (Eds.), Measuring Linguistic Complexity (pp. 65–82). Oxford: Oxford University Press.Google Scholar
Jackendoff, R., & Wittenberg, E. (2017). Linear grammar as a possible stepping-stone in the evolution of language. Psychonomic Bulletin & Review, 24, 219–224.Google Scholar
Jakobson, R., & Vine, B. (1985). Poetry of grammar and grammar of poetry. In Pomorska, K. & Rudy, S. (Eds.), Verbal Art, Verbal Sign, Verbal Time (pp. 37–46). Minneapolis, MN: University of Minnesota Press.Google Scholar
Jurafsky, D., & Martin, J. H. (2009). Speech and Language Processing (2nd ed.). Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Kahneman, D. (2011). Thinking: Fast and Slow. New York, NY: Farrar, Straus & Giroux.Google Scholar
Kempson, R., Meyer-Viol, W., & Gabbay, D. (2001). Dynamic Syntax: The Flow of Language Understanding. Oxford: Wiley-Blackwell.Google Scholar
Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive architectures: research issues and challenges. Cognitive Systems Research, 10, 141–160.Google Scholar
Lee, H., Chang, A., Peirsman, Y., Chambers, N., Surdeanu, M., & Jurafsky, D. (2013). Deterministic coreference resolution based on entity-centric, precision-ranked rules. Computational Linguistics, 39 (4), 885–916.Google Scholar
Lenat, D. B., & Guha, R. (1990). Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project (1st ed.). Boston, MA: Addison-Wesley Longman.Google Scholar
Lenat, D. B., Guha, R. V., Pittman, K., Pratt, D., & Shepherd, M. (1990). Cyc: toward programs with common sense. Communications of ACM, 33 (8), 30–49.Google Scholar
Lepore, E., & Stone, M. (2010). Against metaphorical meaning. Topoi, 29 (2),165–180.Google Scholar
Lieto, A., Lebiere, C., & Oltramari, A. (2018). The knowledge level in cognitive architectures: current limitations and possible developments. Cognitive Systems Research, 48 , 39–55.Google Scholar
Lindes, P., & Laird, J. E. (2016). Toward integrating cognitive linguistics and cognitive language processing. In Reitter, D. & Ritter, F. E. (Eds.), Proceedings of the 14th International Conference on Cognitive Modeling (pp. 86–92).Google Scholar
Marcus, G. (2020). The next decade in AI: four steps towards robust artificial intelligence. arXiv: 2002.06177.Google Scholar
Marr, D. (1982). Vision: A Computational Approach. New York, NY: W. H. Freeman.Google Scholar
McShane, M. (2009). Reference resolution challenges for an intelligent agent: the need for knowledge. IEEE Intelligent Systems, 24 (4), 47–58.CrossRefGoogle Scholar
McShane, M. (2018). Typical event sequences as licensors of direct object ellipsis in Russian. Lingvisticæ Investigationes, 41 (2), 179–212.CrossRefGoogle Scholar
McShane, M., & Leon, I. (2021). Language generation for broad-coverage, explainable cognitive systems. In Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems.Google Scholar
McShane, M., & Nirenburg, S. (2012). A knowledge representation language for natural language processing, simulation and reasoning. International Journal of Semantic Computing, 6 (1), 3–23.Google Scholar
McShane, M., & Nirenburg, S. (2021). Linguistics for the Age of AI. Cambridge: MIT Press. https://direct.mit.edu/books/book/5042/Linguistics-for-the-Age-of-AI.Google Scholar
Newell, A. (1982). The knowledge level. Artificial Intelligence, 18, 87–127.CrossRefGoogle Scholar
Newmeyer, F. J., & Preston, L. B. (2014). Measuring Grammatical Complexity. Oxford: Oxford University Press.Google Scholar
Nirenburg, S., McShane, M., Beale, S., et al. (2018). Toward human-like robot learning. In Natural Language Processing and Information Systems, Proceedings of the 23rd International Conference on Applications of Natural Language to Information Systems (NLDB 2018) (pp. 73–82). Springer.CrossRefGoogle Scholar
Nirenburg, S., McShane, M., & English, J. (2020). Content-centric computational cognitive modeling. In Proceedings of the 2020 Conference on Advances in Cognitive Systems.Google Scholar
Nirenburg, S., Oates, T., & English, J. (2007). Learning by reading by learning to read. In Proceedings of the International Conference on Semantic Computing (pp. 694–701). IEEE Press.Google Scholar
Nirenburg, S., & Raskin, V. (2004). Ontological Semantics. Cambridge: MIT Press.Google Scholar
Nirenburg, S., & Wood, P. (2017). Toward human-style learning in robots. In Proceedings of the AAAI Fall Symposium “Natural Communication for Human-Robot Collaboration.” The AAAI Press.Google Scholar
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021 ). A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32 (2), 604–624. https://doi.org/10.1109/tnnls.2020.2979670CrossRefGoogle ScholarPubMed
Purver, M., Eshghi, A., & Hough, J. (2011). Incremental semantic construction in a dialogue system. In Bos, J. & Pulman, S. (Eds.), Proceedings of the 9th International Conference on Computational Semantics (pp. 365–369). The Association for Computational Linguistics.Google Scholar
Rueschemeyer, S.-A., & Gaskell, M. G. (Eds.) (2018). The Oxford Handbook of Psycholinguistics (2nd ed.). Oxford: Oxford University Press.Google Scholar
Schank, R. C. (1982). Dynamic Memory. Cambridge: Cambridge University Press.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, Plans, Goals, and Understanding. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Scheutz, M., Krause, E., Oosterveld, B., Frasca, T., & Platt, R. (2017). Spoken instruction-based one-shot object and action learning in a cognitive robotic architecture. In Das, S., Durfee, E., Larson, K., & Winikoff, M. (Eds.), Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017).Google Scholar
Spivey, M. J., McRae, K., & Joanisse, M. F. (Eds.) (2012). The Cambridge Handbook of Psycholinguistics. Cambridge: Cambridge University Press.Google Scholar
Zubicaray, G. I. de, & Schiller, N. O. (2019). The Oxford Handbook of Neurolinguistics. Oxford: Oxford University Press.Google Scholar

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