This paper describes the layers of context leveraged by language-endowed intelligent agents (LEIAs) during incremental natural language understanding (NLU). Context is defined as a combination of (a) the perceptual stimuli available to the agent at the given point in time, and (b) the knowledge elements and reasoning activated at the given stage of the agent’s interpretation of those stimuli. This approach to NLU addresses the treatment of a large number of difficult linguistic phenomena that are essential for high-quality NLU but are not being tackled by the knowledge-lean approaches that are typical of modern-day natural language processing. Although LEIAs are being developed as components of prototype application systems, this paper is not about implementations or evaluations – its contribution is conceptual, with everything described applicable to any artificial intelligent agent environment.
Allen, J.F., Chambers, N., et al. (2007). PLOW: A collaborative task learning agent. In Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI’07) (Vol. 2, pp. 1514–1519). AAAI Press.
2.
Ball, J. (2013). Modeling the binding of implicit arguments in complement clauses in ACT-R/Double-R. In R.West and T.Stewart (Eds.), Proceedings of the 12th International Conference on Cognitive Modeling (pp. 84–88). Ottawa: Carleton University.
3.
Baral, C., Lumpkin, B. & Scheutz, M. (2017). A high level language for human robot interaction. In Proceedings of Advances in Cognitive Systems (pp. 1–6).
4.
Byron, D. (2004). Resolving Pronominal Reference to Abstract Entities. PhD Dissertation, University of Rochester. Available as Technical Report 815, University of Rochester Computer Science Department.
5.
Dutta, S. & Basu, A. (2012). A cognitive approach to word sense disambiguation. In A.Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science (Vol. 7181). Berlin, Heidelberg: Springer.
6.
English, J. & Nirenburg, S. (2007). Striking a balance: Human and computer contributions to learning through semantic analysis. In Proceedings of the 4th IEEE International Conference on Semantic Computing, Carnegie Mellon University, Pittsburg PA, September, 2010.
7.
Feldman, J., Dodge, E. & Bryant, J. (2009). A neural theory of language and embodied construction grammar. In B.Heine and H.Narrog (Eds.), The Oxford Handbook of Linguistic Analysis (pp. 111–138). Oxford University Press.
8.
Ferguson, G. & Allen, J. (1998). TRIPS: An integrated intelligent problem-solving assistant. In Proceedings of the National Conference on Artificial Intelligence.
9.
Graff, D. & Cieri, C. (2003). English Gigaword. Philadelphia: Linguistic Data Consortium.
10.
Hirschman, L. & Chinchor, N. (1997). MUC-7 coreference task definition. Version 3. Available at: http://www-nlpir.nist.gov/related_projects/muc/proceedings/co_task.html.
11.
Lakoff, G. & Johnson, M. (1980). Metaphors We Live By. U. of Chicago Press.
12.
Langley, P., Laird, J.E. & Rogers, S. (2009). Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10, 141–160. doi:10.1016/j.cogsys.2006.07.004.
13.
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. doi:10.1162/COLI_a_00152.
14.
Lindes, P. & Laird, J.E. (2016). Toward integrating cognitive linguistics and cognitive language processing. In Proceedings of the 14th International Conference on Cognitive Modeling (ICCM), Pennsylvania. University Park.
15.
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J. & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (pp. 55–60). Stroudsburg, PA: The Association for Computational Linguistics. doi:10.3115/v1/P14-5010.
16.
McShane, M. (2017). Natural language understanding (NLU, not NLP) in cognitive systems. AI Magazine, 34(4), 43–56. doi:10.1609/aimag.v38i4.2745.
17.
McShane, M. (2009). Reference resolution challenges for an intelligent agent: The need for knowledge. IEEE Intelligent Systems, 24(4), 47–58. doi:10.1109/MIS.2009.79.
18.
McShane, M. (2014). Parameterizing mental model ascription across intelligent agents. Interaction Studies, 15(3), 404–425.
19.
McShane, M. (2015). Expectation-driven treatment of difficult referring expressions. In Proceedings of the Third Annual Conference on Advances in Cognitive Systems.
20.
McShane, M. & Babkin, P. (2016a). Detection and resolution of verb phrase ellipsis. Linguistic Issues in Language Technology, 13(1), 1–34.
21.
McShane, M. & Babkin, P. (2016b). Resolving difficult referring expressions. Advances in Cognitive Systems, 4, 247–263.
22.
McShane, M., Beale, S. & Babkin, P. (2014). Nominal compound interpretation by intelligent agents. Linguistic Issues in Language Technology, 10(1), 1–34.
23.
McShane, M., Blissett, K. & Nirenburg, I. (2017). Treating unexpected input in incremental semantic analysis. In Proceedings of the Fifth Annual Conference on Advances in Cognitive Systems.
24.
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. doi:10.1142/S1793351X12400016.
25.
McShane, M., Nirenburg, S. & Beale, S. (2005). An NLP lexicon as a largely language independent resource. Machine Translation, 19(2), 139–173. doi:10.1007/s10590-006-9001-y.
26.
McShane, M., Nirenburg, S. & Beale, S. (2015). The ontological semantic treatment of multiword expressions. Lingvisticæ Investigationes, 38(1), 73–110. doi:10.1075/li.38.1.03mcs.
27.
McShane, M., Nirenburg, S. & Beale, S. (2016). Language understanding with ontological semantics. Advances in Cognitive Systems, 4, 35–55.
28.
Minsky, M. (2006). The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon & Schuster.
29.
Nirenburg, S. & McShane, M. (2016). Natural language processing. In S.E.F.Chipman (Ed.), The Oxford Handbook of Cognitive Science (Vol. 1).
30.
Nirenburg, S., McShane, M. & Beale, S. (2008). A simulated physiological/cognitive “double agent”. In J.Beal, P.Bello, N.Cassimatis, M.Coen and P.Winston (Eds.), Papers from the AAAI Fall Symposium “Naturally Inspired Cognitive Architectures”, Menlo Park, CA: AAAI Press. AAAI Technical Report FS-08-06.
31.
Nirenburg, S., McShane, M., Beale, S., Wood, P., Scassellati, B., Mangin, O. & Roncone, A. (2018). Toward human-like robot learning. In Proceedings of the Twenty Third International Conference on Natural Language and Information Systems (pp. 73–82).
32.
Nirenburg, S. & Raskin, V. (2004). Ontological Semantics. Cambridge, Mass: The MIT Press.
33.
Traum, D.R. (1994). A Computational Theory of Grounding in Natural Language Conversation. PhD thesis, Department of Computer Science, University of Rochester. Also available as TR 545, Department of Computer Science, University of Rochester.
