Abstract is missing.
- The nature of noise in linguistic corporaRandy Goebel, Shane Bergsma, Ying Xu, Christoph Ringlstetter, Mi-Young Kim. 1-2 [doi]
- Document: a useful level for facing noisy dataHervé Déjean, Jean-Luc Meunier. 3-10 [doi]
- A platform for storing, visualizing, and interpreting collections of noisy documentsBart Lamiroy, Daniel P. Lopresti. 11-18 [doi]
- Extracting person names from diverse and noisy OCR textThomas L. Packer, Joshua F. Lutes, Aaron P. Stewart, David W. Embley, Eric K. Ringger, Kevin D. Seppi, Lee S. Jensen. 19-26 [doi]
- The effects of learner errors on the development of a collocation detection toolYoko Futagi. 27-34 [doi]
- Improving accuracy of identifying clinical concepts in noisy unstructured clinical notes using existing internal redundancyJon David Patrick, Pooyan Asgari, Negin Motamedi. 35-42 [doi]
- Clustering based approach to learning regular expressions over large alphabet for noisy unstructured textRohit Babbar, Nidhi Singh. 43-50 [doi]
- Reshaping automatic speech transcripts for robust high-level spoken document analysisJulien Fayolle, Fabienne Moreau, Christian Raymond, Guillaume Gravier. 51-58 [doi]
- Statement map: reducing web information credibility noise through opinion classificationKoji Murakami, Eric Nichols, Junta Mizuno, Yotaro Watanabe, Shouko Masuda, Hayato Goto, Megumi Ohki, Chitose Sao, Suguru Matsuyoshi, Kentaro Inui, Yuji Matsumoto. 59-66 [doi]
- Variant search and syntactic tree similarity based approach to retrieve matching questions for SMS queriesAkhil Langer, Rohit Banga, Ankush Mittal, L. Venkata Subramaniam. 67-72 [doi]
- Discovering users topics of interest on twitter: a first lookMatthew Michelson, Sofus A. Macskassy. 73-80 [doi]
- Tokenizing micro-blogging messages using a text classification approachGustavo Laboreiro, Luís Sarmento, Jorge Teixeira, Eugenio Oliveira. 81-88 [doi]