4. AND 2010:
Toronto,
Ontario,
Canada
Roberto Basili, Daniel P. Lopresti, Christoph Ringlstetter, Shourya Roy, Klaus U. Schulz, L. Venkata Subramaniam (Eds.):
Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, AND 2010, Toronto, Ontario, Canada, October 26th, 2010 (in conjunction with CIKM 2010).
ACM 2010, ISBN 978-1-4503-0376-7
Accepted papers
- Hervé Déjean, Jean-Luc Meunier:
Document: a useful level for facing noisy data.
3-10
- Bart Lamiroy, Daniel P. Lopresti:
A platform for storing, visualizing, and interpreting collections of noisy documents.
11-18
- Thomas L. Packer, Joshua F. Lutes, Aaron P. Stewart, David W. Embley, Eric K. Ringger, Kevin D. Seppi, Lee S. Jensen:
Extracting person names from diverse and noisy OCR text.
19-26
- Yoko Futagi:
The effects of learner errors on the development of a collocation detection tool.
27-34
- Jon David Patrick, Pooyan Asgari, Negin Motamedi:
Improving accuracy of identifying clinical concepts in noisy unstructured clinical notes using existing internal redundancy.
35-42
- Rohit Babbar, Nidhi Singh:
Clustering based approach to learning regular expressions over large alphabet for noisy unstructured text.
43-50
- Julien Fayolle, Fabienne Moreau, Christian Raymond, Guillaume Gravier:
Reshaping automatic speech transcripts for robust high-level spoken document analysis.
51-58
- Koji Murakami, Eric Nichols, Junta Mizuno, Yotaro Watanabe, Shouko Masuda, Hayato Goto, Megumi Ohki, Chitose Sao, Suguru Matsuyoshi, Kentaro Inui, Yuji Matsumoto:
Statement map: reducing web information credibility noise through opinion classification.
59-66
- Akhil Langer, Rohit Banga, Ankush Mittal, L. Venkata Subramaniam:
Variant search and syntactic tree similarity based approach to retrieve matching questions for SMS queries.
67-72
- Matthew Michelson, Sofus A. Macskassy:
Discovering users' topics of interest on twitter: a first look.
73-80
- Gustavo Laboreiro, Luís Sarmento, Jorge Teixeira, Eugenio Oliveira:
Tokenizing micro-blogging messages using a text classification approach.
81-88
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