Simple learning and compositional application of perceptually groundedword meanings for incremental reference resolution

ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference

Volumn 1, Issue , 2015, Pages 292-301

  Kennington, Casey ^a   Schlangen, David ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; SEMANTICS;

DISTRIBUTIONAL SEMANTICS; LOGICAL SEMANTICS; REFERENCE RESOLUTION; SIMILARITY RELATIONS; TRAINING EXAMPLE;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84943801731     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p15-1029     Document Type: Conference Paper

References (28)

1. Elia Bruni, Gemma Boleda, Marco Baroni, and Nam-Khanh Tran. 2012. Distributional semantics in technicolor. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, volume 1, pages 136-145.
2. Elia Bruni, Nam Khanh Tran, and Marco Baroni. 2014. Multimodal distributional semantics. Journal of Artificial Intelligence Research, 49:1-47.
3. Herbert H Clark. 1996. Using Language, volume 23. Cambridge University Press.
4. Nikos Engonopoulos, Martin Villalba, Ivan Titov, and Alexander Koller. 2013. Predicting the resolution of referring expressions from user behavior. In Proceedings of EMLNP, pages 1354-1359, Seattle, Washington, USA. Association for Computational Linguistics.
5. Katrin Erk. 2013. Towards a semantics for distributional representations. In Proceedings of IWCS, pages 1-11, Potsdam, Germany.
6. Charles J Fillmore. 1975. Pragmatics and the description of discourse. Radical pragmatics, pages 143-166.
7. Kotaro Funakoshi, Mikio Nakano, Takenobu Tokunaga, and Ryu Iida. 2012. A Unified Probabilistic Approach to Referring Expressions. In Proceedings of SIGDial, pages 237-246, Seoul, South Korea, July. Association for Computational Linguistics.
8. L T F Gamut. 1991. Logic, Language and Meaning: Intensional Logic and Logical Grammar, volume 2. Chicago University Press, Chicago.
9. Peter Gorniak and Deb Roy. 2004. Grounded semantic composition for visual scenes. Journal of Artificial Intelligence Research, 21:429-470.
10. Stevan Harnad. 1990. The Symbol Grounding Problem. Physica D, 42:335-346.
11. John Kelleher, Fintan Costello, and Jofsef Van Genabith. 2005. Dynamically structuring, updating and interrelating representations of visual and linguistic discourse context. Artificial Intelligence, 167(1-2):62-102.
12. Casey Kennington, Spyros Kousidis, and David Schlangen. 2013. Interpreting Situated Dialogue Utterances: An Update Model that Uses Speech, Gaze, and Gesture Information. In Proceedings of SIGdial.
13. Casey Kennington, Spyros Kousidis, and David Schlangen. 2014. Situated Incremental Natural Language Understanding using a Multimodal, Linguistically-driven Update Model. In Proceedings of CoLing.
14. Casey Kennington, Livia Dia, and David Schlangen. 2015a. A Discriminative Model for Perceptually-Grounded Incremental Reference Resolution. In Proceedings of IWCS. Association for Computational Linguistics.
15. Casey Kennington, Ryu Iida, Takenobu Tokunaga, and David Schlangen. 2015b. Incrementally Tracking Reference in Human/Human Dialogue Using Linguistic and Extra-Linguistic Information. In NAACL, Denver, U.S.A. Association for Computational Linguistics.
16. Ryan Kiros, Ruslan Salakhutdinov, and Richard S Zemel. 2014. Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. In Proceedings of NIPS 2014 Deep Learning Workshop, pages 1-13.
17. Staffan Larsson. 2013. Formal semantics for perceptual classification. Journal of Logic and Computation.
18. Mike Lewis and Mark Steedman. 2013. Combined Distributional and Logical Semantics. Transactions of the ACL, 1:179-192.
19. Edward Loper and Steven Bird. 2002. NLTK: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics-Volume 1, pages 63-70. Association for Computational Linguistics.
20. Cynthia Matuszek, Liefeng Bo, Luke Zettlemoyer, and Dieter Fox. 2014. Learning from Unscripted Deictic Gesture and Language for Human-Robot Interactions. In AAAI. AAAI Press.
21. Jeff Mitchell and Mirella Lapata. 2010. Composition in distributional models of semantics. Cognitive science, 34(8):1388-1429, November.
22. Richard Montague. 1973. The Proper Treatment of Quantifikation in Ordinary English. In J Hintikka, J Moravcsik, and P Suppes, editors, Approaches to Natural Language: Proceedings of the 1970 Stanford Workshop on Grammar and Semantics, pages 221-242, Dordrecht. Reidel.
23. Denis Paperno, Nghia The Pham, and Marco Baroni. 2014. A practical and linguistically-motivated approach to compositional distributional semantics. In Proceedings of ACL, pages 90-99.
24. Barbara H Partee, Alice ter Meuelen, and Robert E Wall. 1993. Mathematical Methods in Linguistics. Kluwer Academic Publishers, Dordrecht.
25. Kari Pulli, Anatoly Baksheev, Kirill Kornyakov, and Victor Eruhimov. 2012. Real-Time computer vision with OpenCV. Communications of the ACM, 55(6):61-69.
26. Richard Socher, Andrej Karpathy, Quoc V Le, Christopher D Manning, and Andrew Y Ng. 2014. Grounded Compositional Semantics for Finding and Describing Images with Sentences. Transactions of the Association for Computational Linguistics (TACL), 2:207-218.
27. Luc Steels and Tony Belpaeme. 2005. Coordinating perceptually grounded categories through language: A case study for colour. The Behavioral and brain sciences, 28(4):469-489; discussion 489-529.
28. Peter D Turney and Patrick Pantel. 2010. From Frequency to Meaning: Vector Space Models of Semantics. Artificial Intelligence, 37(1):141-188