Minimally-supervised extraction of entities from text advertisements

NAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference

Volumn , Issue , 2010, Pages 73-81

  Singh, Sameer ^a   Hillard, Dustin ^b   Leggetter, Chris ^b  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

^b YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY IMPROVEMENT; COMPUTATIONAL ADVERTISINGS; CONDITIONAL RANDOM FIELD; CONSTRAINT-BASED; CREATIVES; ENTITY EXTRACTIONS; LABELED DATA; LARGE AMOUNTS OF DATA; LIGHT WEIGHT; ONLINE LEARNING ALGORITHMS; SEMI-MARKOV; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SUPERVISED CLASSIFIERS; TEST SETS; UNLABELED DATA;

LEARNING ALGORITHMS;

COMPUTATIONAL LINGUISTICS;

EID: 84863338186     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Eugene Agichtein and Venkatesh Ganti. 2004. Mining reference tables for automatic text segmentation. In KDD: ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pages 20-29, New York, NY, USA.
2. Kedar Bellare, Gregory Druck, and Andrew McCallum. 2009. Alternating projections for learning with expectation constraints. In UAI: Conference on Uncertainty in Artificial Intelligence.
3. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal on Machine Learning Research, 3:993-1022.
4. Mary Elaine Califf and Raymond J. Mooney. 1999. Relational learning of pattern-match rules for information extraction. In AAAI / IAAI '99: National conference on Artificial intelligence and the Innovative Applications of Artificial Intelligence conference, pages 328-334.
5. Sander Canisius and Caroline Sporleder. 2007. Bootstrapping information extraction from field books. In EMNLP-CoNLL: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 827-836.
6. Ming-Wei Chang, Lev Ratinov, and Dan Roth. 2007. Guiding semi-supervision with constraint-driven learning. In ACL: Annual meeting of the Association for Computational Linguistics, pages 280-287.
7. Ming-Wei Chang, Lev Ratinov, Nicholas Rizzolo, and Dan Roth. 2008. Learning and inference with constraints. In AAAI: National Conference on Artificial Intelligence, pages 1513-1518.
8. O. Chapelle, B. Schölkopf, and A. Zien, editors. 2006. Semi-Supervised Learning (Adaptive Computation and Machine Learning). The MIT Press, September.
9. Massimiliano Ciaramita, Vanessa Murdock, and Vassilis Plachouras. 2008. Online learning from click data for sponsored search. In WWW: International World Wide Web Conference.
10. Dustin Hillard, Stefan Schroedl, Eren Manavoglu, Hema Raghavan, and Chris Leggetter. 2010. Improving ad relevance in sponsored search. In WSDM: International conference on Web search and data mining, pages 361-370.
11. John Lafferty, Andrew Mccallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML: International Conference on Machine Learning, pages 282-289.
12. Xiao Li, Ye-Yi Wang, and Alex Acero. 2009. Extracting structured information from user queries with semi-supervised conditional random fields. In SIGIR: International Conference on research and development in information retrieval, pages 572-579. ACM.
13. Gideon S. Mann and Andrew McCallum. 2008. Generalized expectation criteria for semi-supervised learning of conditional random fields. In ACL: Annual meeting of the Association for Computational Linguistics, pages 870-878.
14. Andrew McCallum. 2002. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu.
15. Ion Muslea. 1999. Extraction patterns for information extraction tasks: A survey. In AAAI: Workshop on Machine Learning for Information Extraction, pages 1-6.
16. Patrick Pantel, Eric Crestan, Arkady Borkovsky, AnaMaria Popescu, and Vishnu Vyas. 2009. Web-scale distributional similarity and entity set expansion. In EMNLP: Conference on Empirical Methods in Natural Language Processing, pages 938-947.
17. Hema Raghavan and Rukmini Iyer. 2008. Evaluating vector-space and probabilistic models for query to ad matching. In SIGIR Workshop on Information Retrieval in Advertising (IRA).
18. Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007. Predicting clicks: estimating the click-through rate for new ads. In WWW: International World Wide Web Conference.
19. Sunita Sarawagi and William W. Cohen. 2004. Semi-markov conditional random fields for information extraction. In NIPS: Neural Information Processing Systems.
20. Sunita Sarawagi. 2006. Efficient inference on sequence segmentation models. In ICML: International Conference on Machine Learning, pages 793-800.
21. D. Sculley, Robert G. Malkin, Sugato Basu, and Roberto J. Bayardo. 2009. Predicting bounce rates in sponsored search advertisements. In KDD: ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pages 1325-1334.
22. Benyah Shaparenko, Ozgur Cetin, and Rukmini Iyer. 2009. Data driven text features for sponsored search click prediction. In AdKDD: Workshop on Data mining and audience intelligence for advertising.
23. Sameer Singh, Limin Yao, Sebastian Riedel, and Andrew McCallum. 2010. Constraint-driven rank-based learning for information extraction. In North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT).
24. Mark Steyvers and Tom Griffiths. 2007. Probabilistic Topic Models. Lawrence Erlbaum Associates.