Percy Liang
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Abstract
Semantic parsing is a rich fusion of the logical and the statistical worlds.
- Artzi, Y., Zettlemoyer, K.L.L. Broad-coverage CCG semantic parsing with AMR. In Empirical Methods in Natural Language Processing (2015).Google Scholar
Cross Ref
- Artzi, Y., Zettlemoyer, L. Weakly supervised learning of semantic parsers for mapping instructions to actions. Trans. Assoc.Computat. Linguist. 1 (2013): 49--62.Google ScholarCross Ref
- BanaRescu, L., Cai, C.B.S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Koehn, P., Palmer, M., Schneider, N. Abstract meaning representation for sembanking. In 7th Linguistic Annotation Workshop and Interoperability with Discourse (2013).Google Scholar
- Berant, J., Chou, A., Frostig, R., Liang, P. Semantic parsing on Freebase from question-answer pairs. In Empirical Methods in Natural Language Processing (2013).Google Scholar
- Berant, J., Liang, P. Semantic parsing via paraphrasing. In Association for Computational Linguistics (2014).Google ScholarCross Ref
- Berant, J., Liang, P. Imitation learning of agenda-based semantic parsers. Trans. Assoc. Comput. Linguist. (2015).Google Scholar
- Blackburn, P., Bos, J. Representation and Inference for Natural Language: A First Course in Computational Semantics. CSLI Publishers, 2005.Google Scholar
- Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J. Freebase: A collaboratively created graph database for structuring human knowledge. In International Conference on Management of Data (SIGMOD) (2008), 1247--1250. Google ScholarDigital Library
- Branavan, S., Chen, H., Zettlemoyer, L.S., Barzilay, R. Reinforcement learning for mapping instructions to actions. In Association for Computational Linguistics and International Joint Conference on Natural Language Processing (2009), 82--90. Google ScholarDigital Library
- Brill, E., Dumais, S., Banko, M. An analysis of the AskMSR question-answering system. In Association for Computational Linguistics (2002), 257--264. Google ScholarDigital Library
- Cai, Q., Yates, A. Large-scale semantic parsing via schema matching and lexicon extension. In Association for Computational Linguistics (2013).Google Scholar
- Chen, D.L., Mooney, R.J. Learning to interpret natural language navigation instructions from observations. In Association for the Advancement of Artificial Intelligence (2011), 859--865. Google ScholarDigital Library
- Clarke, J., Goldwasser, D., Chang, M., Roth, D. Driving semantic parsing from the world's response. In Computational Natural Language Learning (2010), 18--27. Google ScholarDigital Library
- Duchi, J., Hazan, E., Singer, Y. Adaptive subgradient methods for online learning and stochastic optimization. Conference on Learning Theory (2010).Google Scholar
- Krishnamurthy, J., Kollar, T. Jointly learning to parse and perceive: Connecting natural language to the physical world. Trans. Assoc. Computat. Linguist. 1 (2013), 193--206.Google ScholarCross Ref
- Krishnamurthy, J., Mitchell, T. Weakly supervised training of semantic parsers. In Empirical Methods in Natural Language Processing and Computational Natural Language Learning (2012), 754--765. Google ScholarDigital Library
- Kushman, N., Barzilay, R. Using semantic unification to generate regular expressions from natural language. In Human Language Technology and North American Association for Computational Linguistics (2013), 826--836.Google Scholar
- Kwiatkowski, T., Choi, E., Artzi, Y., Zettlemoyer, L. Scaling semantic parsers with on-the-fly ontology matching. In Empirical Methods in Natural Language Processing (2013).Google Scholar
- Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., Steedman, M. Inducing probabilistic CCG grammars from logical form with higher-order unification. In Empirical Methods in Natural Language Processing (2010), 1223--1233. Google ScholarDigital Library
- Liang, P. Lambda dependency-based compositional semantics. arXiv, 2013.Google Scholar
- Liang, P., Jordan, M.I., Klein, D. Learning dependency-based compositional semantics. In Association for Computational Linguistics (ACL) (2011), 590--599. Google ScholarDigital Library
- Liang, P., Potts, C. Bringing machine learning and compositional semantics together. Annu. Rev. Linguist. 1, 1 (2015), 355--376.Google ScholarCross Ref
- Matuszek, C., FitzGerald, N., Zettlemoyer, L., Bo, L., Fox, D. A joint model of language and perception for grounded attribute learning. In International Conference on Machine Learning (2012), 1671--1678.Google Scholar
- Miller, S., Stallard, D., Bobrow, R., Schwartz, R. A fully statistical approach to natural language interfaces. In Association for Computational Linguistics (1996), 55--61. Google ScholarDigital Library
- Montague, R. The proper treatment of quantification in ordinary English. In Approaches to Natural Language (1973), 221--242.Google ScholarCross Ref
- Pasupat, P., Liang, P. Compositional semantic parsing on semi-structured tables. In Association for Computational Linguistics (2015).Google ScholarCross Ref
- Reddy, S., Lapata, M., Steedman, M. Large-scale semantic parsing without question-answer pairs. Trans. Assoc. Comput. Linguist. 2, 10 (2014), 377--392.Google Scholar
- Steedman, M. The Syntactic Process. MIT Press, 2000. Google ScholarDigital Library
- Tellex, S., Kollar, T., Dickerson, S., Walter, M.R., Banerjee, A.G., Teller, S.J., Roy, N. Understanding natural language commands for robotic navigation and mobile manipulation. In Association for the Advancement of Artificial Intelligence (2011). Google ScholarDigital Library
- Wang, Y., Berant, J., Liang, P. Building a semantic parser overnight. In Association for Computational Linguistics (2015).Google ScholarCross Ref
- Weston, J., Chopra, S., Bordes, A. Memory networks. In International Conference on Learning Representations (2015).Google Scholar
- Winograd, T. Understanding Natural Language. Academic Press, 1972. Google ScholarDigital Library
- Wong, Y.W., Mooney, R.J. Learning synchronous grammars for semantic parsing with lambda calculus. In Association for Computational Linguistics (2007), 960--967.Google Scholar
- Woods, W.A., Kaplan, R.M., Webber, B.N. The lunar sciences natural language information system: Final report. Technical report, BBN Report 2378, Bolt Beranek and Newman Inc., 1972.Google Scholar
- Yih, W., Chang, M., He, X., Gao, J. Semantic parsing via staged query graph generation: Question answering with knowledge base. In Association for Computational Linguistics (2015).Google Scholar
- Zelle, M., Mooney, R.J. Learning to parse database queries using inductive logic programming. In Association for the Advancement of Artificial Intelligence (1996), 1050--1055. Google ScholarDigital Library
- Zettlemoyer, L.S., Collins, M. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Uncertainty in Artificial Intelligence (2005), 658--666.Google Scholar
- Zettlemoyer, L.S., Collins, M. Online learning of relaxed CCG grammars for parsing to logical form. In Empirical Methods in Natural Language Processing and Computational Natural Language Learning (2007), 678--687.Google Scholar
- Zettlemoyer, L.S., Collins, M. Learning context-dependent mappings from sentences to logical form. In Association for Computational Linguistics and International Joint Conference on Natural Language Processing (2009). Google ScholarDigital Library
- Zhao, K., Huang, L. Type-driven incremental semantic parsing with polymorphism. In North American Association for Computational Linguistics (2015).Google ScholarCross Ref
Index Terms
- Learning executable semantic parsers for natural language understanding
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