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Introduction to the special issue on learning semantics. (English) Zbl 1298.00233

From the text: The NIPS’11 workshop on Learning Semantics1 attracted substantial numbers of attendees (around 80) coming from different backgrounds and with different viewpoints. This special issue was created in order to collect and expose some of this disparate work. Our goal is to provide a snapshot of the current state of this emerging field, and to serve as a springboard for future directions.

MSC:

00B25 Proceedings of conferences of miscellaneous specific interest
68-06 Proceedings, conferences, collections, etc. pertaining to computer science
68T05 Learning and adaptive systems in artificial intelligence
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References:

[1] Artzi, Y.; Zettlemoyer, L., Bootstrapping semantic parsers from conversations (2011)
[2] Bordes, A.; Glorot, X., Joint learning of words and meaning representations for open-text semantic parsing (2012)
[3] Bordes, A.; Usunier, N.; Collobert, R.; Weston, J., Towards understanding situated natural language (2010)
[4] Chen, D. L.; Mooney, R. J., Panning for gold: finding relevant semantic content for grounded language learning (2011)
[5] Clarke, J.; Goldwasser, D.; Chang, M.; Roth, D., Driving semantic parsing from the world’s response (2010)
[6] Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12, 2493-2537. · Zbl 1280.68161
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[8] Farhadi, A.; Endres, I.; Hoiem, D.; Forsyth, D., Describing objects by their attributes, 1778-1785 (2009), New York · Zbl 1157.80401 · doi:10.1109/CVPR.2009.5206772
[9] Felzenszwalb, P., & McAllester, D. (2010). Object detection grammars (Computer Science TR-2010-02, tech. rep.). University of Chicago.
[10] Goldwasser, D.; Reichart, R.; Clarke, J.; Roth, D., Confidence driven unsupervised semantic parsing (2011)
[11] Gupta, A.; Efros, A.; Hebert, M., Blocks world revisited: image understanding using qualitative geometry and mechanics, 482-496 (2010) · doi:10.1007/978-3-642-15561-1_35
[12] Kate, R. J.; Mooney, R. J., Learning language semantics from ambiguous supervision, No. 22, 895 (2007), Menlo Park
[13] Lampert, C. H.; Nickisch, H.; Harmeling, S., Learning to detect unseen object classes by between-class attribute transfer, 951-958 (2009), New York · doi:10.1109/CVPR.2009.5206594
[14] Lewis, D. (1970). General semantics. Synthese, 22, 18-67. doi:10.1007/BF00413598. · Zbl 0214.00406 · doi:10.1007/BF00413598
[15] Liang, P.; Jordan, M. I.; Klein, D., Learning dependency-based compositional semantics (2011)
[16] Matuszek, C.; FitzGerald, N.; Zettlemoyer, L.; Bo, L.; Fox, D., A joint model of language and perception for grounded attribute learning (2012)
[17] Miller, S.; Stallard, D.; Bobrow, R.; Schwartz, R., A fully statistical approach to natural language interfaces (1996)
[18] Mitchell, J.; Lapata, M., Vector-based models of semantic composition, 236-244 (2008)
[19] Poon, H.; Domingos, P., Unsupervised ontology induction from text, 296-305 (2010), Stroudsburg
[20] Socher, R.; Lin, C. C.; Ng, A. Y.; Manning, C. D., Parsing natural scenes and natural language with recursive neural networks (2011)
[21] Zelle, J.; Mooney, R., Learning to parse database queries using inductive logic programming (1996)
[22] Zettlemoyer, L.; Collins, M., Learning to map sentences to logical form: structured classification with probabilistic categorial grammars (2005)
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