• April 2015: finish annotating the behavior labels for the insurance data
• June 2015: implement behavior prediction
• July 2015: submit a conference paper about behavior prediction
• August 2015: submit a journal paper
• September 2015: finish thesis writing
• October 2015: thesis defense
• December 2015: finish thesis revision
Bibliography
[1] James F Allen, Donna K Byron, Myroslava Dzikovska, George Ferguson, Lucian Galescu, and Amanda Stent. Toward conversational human-computer interaction. AI magazine, 22(4):27, 2001.
[2] Collin Baker. Framenet, present and future. Programme Committee 7, 2008.
[3] Collin F Baker, Charles J Fillmore, and John B Lowe. The Berkeley FrameNet project.
In Proceedings of COLING, pages 86–90, 1998.
[4] Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Herm-jakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. Abstract meaning representation for sembanking. In Proceedings of Language Annotation Work-shop, 2013.
[5] Marco Baroni, Georgiana Dinu, and Germ´an Kruszewski. Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguis-tics, volume 1, 2014.
[6] Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. Semantic parsing on freebase from question-answer pairs. In EMNLP, pages 1533–1544, 2013.
[7] Dan Bohus and Alexander I Rudnicky. LARRI: A language-based maintenance and re-pair assistant. In Spoken multimodal human-computer dialogue in mobile environments, pages 203–218. Springer, 2005.
[8] Dan Bohus and Alexander I Rudnicky. The RavenClaw dialog management framework:
Architecture and systems. Computer Speech & Language, 23(3):332–361, 2009.
[9] Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. Free-base: a collaboratively created graph database for structuring human knowledge. In Proceedings of International Conference on Management of Data, 2008.
[10] Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio, et al. Learning struc-tured embeddings of knowledge bases. In Proceedings of AAAI, 2011.
[11] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Proceedings of NIPS, 2013.
[12] Kendrick Boyd, Vitor Santos Costa, Jesse Davis, and C David Page. Unachievable region in precision-recall space and its effect on empirical evaluation. In Machine learn-ing: proceedings of the International Conference. International Conference on Machine Learning, volume 2012, page 349. NIH Public Access, 2012.
[13] Sergey Brin and Lawrence Page. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1):107–117, 1998.
[14] Peter F Brown, Peter V Desouza, Robert L Mercer, Vincent J Della Pietra, and Jenifer C Lai. Class-based n-gram models of natural language. Computational linguistics, 18(4):
467–479, 1992.
[15] Asli Celikyilmaz, Dilek Hakkani-T¨ur, and Gokhan T¨ur. Leveraging web query logs to learn user intent via bayesian discrete latent variable model. 2011.
[16] Yun-Nung Chen and Florian Metze. Two-layer mutually reinforced random walk for im-proved multi-party meeting summarization. In Proceedings of The 4th IEEE Workshop on Spoken Language Tachnology, pages 461–466, 2012.
[17] Yun-Nung Chen and Florian Metze. Multi-layer mutually reinforced random walk with hidden parameters for improved multi-party meeting summarization. In INTER-SPEECH, pages 485–489, 2013.
[18] Yun-Nung Chen and Alexander I. Rudnicky. Dynamically supporting unexplored do-mains in conversational interactions by enriching semantics with neural word embed-dings. In Proceedings of SLT, 2014.
[19] Yun-Nung Chen, William Yang Wang, and Alexander I. Rudnicky. An empirical investi-gation of sparse log-linear models for improved dialogue act classification. In Proceedings of ICASSP, pages 8317–8321, 2013.
[20] Yun-Nung Chen, William Yang Wang, and Alexander I Rudnicky. Unsupervised in-duction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing. In Proceedings of ASRU, pages 120–125, 2013.
[21] Yun-Nung Chen, Dilek Hakkani-T¨ur, and Gokhan Tur. Deriving local relational sur-face forms from dependency-based entity embeddings for unsupervised spoken language understanding. In Proceedings of SLT, 2014.
[22] Yun-Nung Chen, William Yang Wang, and Alexander I Rudnicky. Leveraging frame se-mantics and distributional sese-mantics for unsupervised semantic slot induction in spoken dialogue systems. In Proceedings of SLT, 2014.
[23] Yun-Nung Chen, William Yang Wang, and Alexander I. Rudnicky. Jointly modeling inter-slot relations by random walk on knowledge graphs for unsupervised spoken lan-guage understanding. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo-gies, 2015.
[24] Ananlada Chotimongkol. Learning the structure of task-oriented conversations from the corpus of in-domain dialogs. PhD thesis, Carnegie Mellon University, 2008.
[25] Michael Collins, Sanjoy Dasgupta, and Robert E Schapire. A generalization of principal components analysis to the exponential family. In Advances in neural information processing systems, pages 617–624, 2001.
[26] Bob Coyne, Daniel Bauer, and Owen Rambow. VigNet: Grounding language in graphics using frame semantics. In Proceedings of the ACL 2011 Workshop on Relational Models of Semantics, pages 28–36, 2011.
[27] Dipanjan Das, Nathan Schneider, Desai Chen, and Noah A Smith. Probabilistic frame-semantic parsing. In Proceedings of NAACL-HLT, pages 948–956, 2010.
[28] Dipanjan Das, Desai Chen, Andr´e F. T. Martins, Nathan Schneider, and Noah A. Smith.
Frame-semantic parsing. Computational Linguistics, 2013.
[29] Dipanjan Das, Desai Chen, Andr´e FT Martins, Nathan Schneider, and Noah A Smith.
Frame-semantic parsing. Computational Linguistics, 40(1):9–56, 2014.
[30] Marie-Catherine De Marneffe and Christopher D Manning. The Stanford typed de-pendencies representation. In Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation, pages 1–8. Association for Compu-tational Linguistics, 2008.
[31] Renato De Mori, Fr´ed´eric Bechet, Dilek Hakkani-Tur, Michael McTear, Giuseppe Ric-cardi, and Gokhan Tur. Spoken language understanding. Signal Processing Magazine, IEEE, 25(3), 2008.
[32] Scott C. Deerwester, Susan T Dumais, Thomas K. Landauer, George W. Furnas, and Richard A. Harshman. Indexing by latent semantic analysis. JASIS, 41(6):391–407, 1990.
[33] Li Deng, Gokhan Tur, Xiaodong He, and Dilek Hakkani-T¨ur. Use of kernel deep convex networks and end-to-end learning for spoken language understanding. In Proceedings of SLT, pages 210–215, 2012.
[34] John Dowding, Jean Mark Gawron, Doug Appelt, John Bear, Lynn Cherny, Robert Moore, and Douglas Moran. Gemini: A natural language system for spoken-language understanding. In Proceedings of the 31st annual meeting on Association for Computa-tional Linguistics, pages 54–61. Association for ComputaComputa-tional Linguistics, 1993.
[35] Ali El-Kahky, Derek Liu, Ruhi Sarikaya, Gokhan Tur, Dilek Hakkani-T¨ur, and Larry Heck. Extending domain coverage of language understanding systems via intent transfer between domains using knowledge graphs and search query click logs. In Proceedings of ICASSP, 2014.
[36] Benoit Favre, Dilek Hakkani-T¨ur, and Sebastien Cuendet. Icsiboost. 2007. URL http:
//code.google.come/p/icsiboost.
[37] Charles Fillmore. Frame semantics. Linguistics in the morning calm, pages 111–137, 1982.
[38] Charles J Fillmore. Frame semantics and the nature of language. Annals of the NYAS, 280(1):20–32, 1976.
[39] Jeffrey Flanigan, Sam Thomson, Jaime Carbonell, Chris Dyer, and Noah A Smith. A discriminative graph-based parser for the abstract meaning representation. In Proceed-ings of the Annual Meeting of the Association for Computational Linguistics. Citeseer, 2014.
[40] GW Furnas, TK Landauer, LM Gomez, and ST Dumais. Statistical semantics: Analysis of the potential performance of keyword information systems. In Proceedings of Human Factors in Computer Systems, pages 187–242, 1984.
[41] Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme.
Mymedialite: A free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems, pages 305–308. ACM, 2011.
[42] Narendra Gupta, Gokhan Tur, Dilek Hakkani-Tur, Srinivas Bangalore, Giuseppe Ric-cardi, and Mazin Gilbert. The AT&T spoken language understanding system. IEEE Transactions on Audio, Speech, and Language Processing, 14(1):213–222, 2006.
[43] Dilek Hakkani-T¨ur, Fr´ed´eric B´echet, Giuseppe Riccardi, and Gokhan Tur. Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech & Language, 20(4):495–514, 2006.
[44] Dilek Hakkani-T¨ur, Asli Celikyilmaz, Larry P Heck, and Gokhan Tur. A weakly-supervised approach for discovering new user intents from search query logs. In Pro-ceedings of INTERSPEECH, 2013.
[45] Dilek Hakkani-T¨ur, Larry Heck, and Gokhan Tur. Using a knowledge graph and query click logs for unsupervised learning of relation detection. In Proceedings of ICASSP, pages 8327–8331, 2013.
[46] Dilek Hakkani-Tur, Asli Celikyilmaz, Larry Heck, Gokhan Tur, and Geoff Zweig. Prob-abilistic enrichment of knowledge graph entities for relation detection in conversational understanding. In Proceedings of INTERSPEECH, 2014.
[47] Zellig S Harris. Distributional structure. Word, 1954.
[48] Kazi Saidul Hasan and Vincent Ng. Frame semantics for stance classification. CoNLL-2013, page 124, 2013.
[49] Larry Heck and Dilek Hakkani-T¨ur. Exploiting the semantic web for unsupervised spoken language understanding. In Proceedings of SLT, pages 228–233, 2012.
[50] Larry P Heck, Dilek Hakkani-T¨ur, and Gokhan Tur. Leveraging knowledge graphs for web-scale unsupervised semantic parsing. In Proceedings of INTERSPEECH, 2013.
[51] Steffen Hedegaard and Jakob Grue Simonsen. Lost in translation: authorship attribu-tion using frame semantics. In Proceedings of ACL-HLT, pages 65–70, 2011.
[52] Matthew Henderson, Milica Gasic, Blaise Thomson, Pirros Tsiakoulis, Kai Yu, and Steve Young. Discriminative spoken language understanding using word confusion net-works. In Proceedings of SLT, pages 176–181, 2012.
[53] Dustin Hillard, Asli Celikyilmaz, Dilek Z Hakkani-T¨ur, and Gokhan Tur. Learning weighted entity lists from web click logs for spoken language understanding. In Pro-ceedings of INTERSPEECH, 2011.
[54] Diana Inkpen and Graeme Hirst. Building and using a lexical knowledge base of near-synonym differences. Computational Linguistics, 32(2):223–262, 2006.
[55] Frederick Jelinek. Statistical methods for speech recognition. MIT press, 1997.
[56] Richard Johansson and Pierre Nugues. Extended constituent-to-dependency conversion for english. In 16th Nordic Conference of Computational Linguistics. University of Tartu, 2007.
[57] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, (8):30–37, 2009.
[58] Amy N Langville and Carl D Meyer. A survey of eigenvector methods for web informa-tion retrieval. SIAM review, 47(1):135–161, 2005.
[59] Ni Lao, Tom Mitchell, and William W Cohen. Random walk inference and learning in a large scale knowledge base. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 529–539. Association for Computational Linguis-tics, 2011.
[60] Ni Lao, Amarnag Subramanya, Fernando Pereira, and William W Cohen. Reading the web with learned syntactic-semantic inference rules. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Compu-tational Natural Language Learning, pages 1017–1026. Association for CompuCompu-tational Linguistics, 2012.
[61] Staffan Larsson and David R Traum. Information state and dialogue management in the TRINDI dialogue move engine toolkit. Natural language engineering, 6(3&4):323–340, 2000.
[62] Fabrice Lefevre, Fran¸cois Mairesse, and Steve Young. Cross-lingual spoken language un-derstanding from unaligned data using discriminative classification models and machine translation. In INTERSPEECH, 2010.
[63] Oliver Lemon, Kallirroi Georgila, James Henderson, and Matthew Stuttle. An ISU dialogue system exhibiting reinforcement learning of dialogue policies: generic slot-filling in the talk in-car system. In Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations, pages 119–122. Association for Computational Linguistics, 2006.
[64] Omer Levy and Yoav Goldberg. Dependency-based word embeddings. In Proceedings of ACL, 2014.
[65] Peipei Li, Haixun Wang, Hongsong Li, and Xindong Wu. Assessing sparse information extraction using semantic contexts. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, pages 1709–1714.
ACM, 2013.
[66] Peipei Li, Haixun Wang, Kenny Q Zhu, Zhongyuan Wang, and Xindong Wu. Computing term similarity by large probabilistic isa knowledge. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, pages 1401–1410. ACM, 2013.
[67] Percy Liang. Semi-supervised learning for natural language. PhD thesis, Massachusetts Institute of Technology, 2005.
[68] Tom´aˇs Mikolov. Statistical language models based on neural networks. PhD thesis, Brno University of Technology, 2012.
[69] Tom´aˇs Mikolov, Martin Karafi´at, Lukas Burget, Jan Cernock`y, and Sanjeev Khudanpur.
Recurrent neural network based language model. In Proceedings of INTERSPEECH, pages 1045–1048, 2010.
[70] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. In Proceedings of Workshop at ICLR, 2013.
[71] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Proceedings of Advances in Neural Information Processing Systems, 2013.
[72] Tom´aˇs Mikolov, Wen-tau Yih, and Geoffrey Zweig. Linguistic regularities in continuous space word representations. In Proceedings of NAACL-HLT, pages 746–751, 2013.
[73] Scott Miller, Robert Bobrow, Robert Ingria, and Richard Schwartz. Hidden under-standing models of natural language. In Proceedings of the 32nd annual meeting on Association for Computational Linguistics, pages 25–32. Association for Computational Linguistics, 1994.
[74] J. Pasternack and D. Roth. The wikipedia corpus, 2008.
[75] Fabian Pedregosa, Ga¨el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12:2825–2830, 2011.
[76] Slav Petrov and Dan Klein. Learning and inference for hierarchically split pcfgs. In Proceedings of the National Conference on Artificial Intelligence, 2007.
[77] Roberto Pieraccini, Evelyne Tzoukermann, Zakhar Gorelov, J Gauvain, Esther Levin, Chin-Hui Lee, and Jay G Wilpon. A speech understanding system based on statisti-cal representation of semantics. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 193–196. IEEE, 1992.
[78] Patti Price. Evaluation of spoken language systems: The ATIS domain. In Proceedings of the Third DARPA Speech and Natural Language Workshop, pages 91–95. Morgan Kaufmann, 1990.
[79] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme.
Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pages 452–461. AUAI Press, 2009.
[80] Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M Marlin. Relation extraction with matrix factorization and universal schemas. 2013.
[81] Alexander I Rudnicky, Eric H Thayer, Paul C Constantinides, Chris Tchou, R Shern, Kevin A Lenzo, Wei Xu, and Alice Oh. Creating natural dialogs in the carnegie mellon communicator system.
[82] Stephanie Seneff. TINA: A natural language system for spoken language applications.
Computational linguistics, 18(1):61–86, 1992.
[83] Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(8):888–905, 2000.
[84] Anders Skrondal and Sophia Rabe-Hesketh. Generalized latent variable modeling: Mul-tilevel, longitudinal, and structural equation models. Crc Press, 2004.
[85] Richard Socher, John Bauer, Christopher D Manning, and Andrew Y Ng. Parsing with compositional vector grammars. In In Proceedings of the ACL conference, 2013.
[86] Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. Recursive deep models for semantic composi-tionality over a sentiment treebank. In Proceedings of EMNLP, pages 1631–1642, 2013.
[87] Yangqiu Song, Haixun Wang, Zhongyuan Wang, Hongsong Li, and Weizhu Chen.
Short text conceptualization using a probabilistic knowledgebase. In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Three, pages 2330–2336. AAAI Press, 2011.
[88] Gokhan Tur. Multitask learning for spoken language understanding. In Proceedings of ICASSP, 2006.
[89] Gokhan Tur, Dilek Z Hakkani-T¨ur, Dustin Hillard, and Asli Celikyilmaz. Towards unsupervised spoken language understanding: Exploiting query click logs for slot filling.
In Proceedings of INTERSPEECH, 2011.
[90] Gokhan Tur, Li Deng, Dilek Hakkani-T¨ur, and Xiaodong He. Towards deeper under-standing: deep convex networks for semantic utterance classification. In Proceedings of ICASSP, pages 5045–5048, 2012.
[91] Gokhan Tur, Minwoo Jeong, Ye-Yi Wang, Dilek Hakkani-T¨ur, and Larry P Heck. Ex-ploiting the semantic web for unsupervised natural language semantic parsing. In Pro-ceedings of INTERSPEECH, 2012.
[92] Gokhan Tur, Asli Celikyilmaz, and Dilek Hakkani-Tur. Latent semantic modeling for slot filling in conversational understanding. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 8307–8311. IEEE, 2013.
[93] Joseph Turian, Lev Ratinov, and Yoshua Bengio. Word representations: a simple and general method for semi-supervised learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 384–394. Association for Computational Linguistics, 2010.
[94] Fang Wang, Zhongyuan Wang, Zhoujun Li, and Ji-Rong Wen. Concept-based short text classification and ranking. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 1069–1078. ACM, 2014.
[95] Lu Wang, Dilek Hakkani-T¨ur, and Larry Heck. Leveraging semantic web search and browse sessions for multi-turn spoken dialog systems. In Proceedings of ICASSP, 2014.