Result 1 to 20 of 43 total
Use of permutation prefixes for efficient and scalable approximate similarity search. (English)
Inf. Process. Manage. 48, No. 5, 889-902 (2012).
1
Enhancing opinion extraction by automatically annotated lexical resources. (Extended version). (English)
Vetulani, Zygmunt (ed.), Human language technology. Challenges for computer science and linguistics. 4th language and technology conference, LTC 2009, Poznan, Poland, November 6‒8, 2009. Revised selected papers. Berlin: Springer (ISBN 978-3-642-20094-6/pbk). Lecture Notes in Computer Science 6562. Lecture Notes in Artificial Intelligence, 500-511 (2011).
2
Blog distillation via sentiment-sensitive link analysis (English)
IIR (2011).
3
Using micro-documents for feature selection: the case of ordinal text classification (English)
IIR (2011).
4
Evaluating information extraction. (English)
Agosti, Maristella (ed.) et al., Multilingual and multimodal information access evaluation. International conference of the cross-language evaluation forum, CLEF 2010, Padua, Italy, September 20‒23, 2010. Proceedings. Berlin: Springer (ISBN 978-3-642-15997-8/pbk). Lecture Notes in Computer Science 6360, 100-111 (2010).
5
Extracting information from free-text mammography reports (English)
ERCIM News 2010, No. 82, 60 (2010).
6
Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining (English)
LREC (2010).
7
Sentence-based active learning strategies for information extraction (English)
IIR, 41-45 (2010).
8
Selecting features for ordinal text classification (English)
IIR, 13-14 (2010).
9
PP-index: using permutation prefixes for efficient and scalable similarity search (Extended abstract) (English)
SEBD, 318-325 (2010).
10
Feature selection for ordinal regression (English)
SAC, 1748-1754 (2010).
11
Evaluating information extraction (English)
CLEF, 100-111 (2010).
12
Cophir: a test collection for content-based image retrieval. (English)
Comput. Res. Repos. 2009, Article No. 0905.4627 (2009).
13
Training data cleaning for text classification. (English)
Azzopardi, Leif (ed.) et al., Advances in information retrieval theory. Second international conference on the theory of information retrieval, ICTIR 2009, Cambridge, UK, September 10‒12, 2009. Proceedings. Berlin: Springer (ISBN 978-3-642-04416-8/pbk). Lecture Notes in Computer Science 5766, 29-41 (2009).
14
Automatically determining attitude type and force for sentiment analysis. (English)
Vetulani, Zygmunt (ed.) et al., Human language technology. Challenges of the information society. Third language and technology conference, LTC 2007, Poznan, Poland, October 5‒7, 2007. Revised selected papers. Berlin: Springer (ISBN 978-3-642-04234-8/pbk). Lecture Notes in Computer Science 5603. Lecture Notes in Artificial Intelligence, 218-231 (2009).
15
Encoding ordinal features into binary features for text classification. (English)
Boughanem, Mohand (ed.) et al., Advances in information retrieval. 31th European conference on IR research, ECIR 2009, Toulouse, France, April 6‒9, 2009. Proceedings. Berlin: Springer (ISBN 978-3-642-00957-0/pbk). Lecture Notes in Computer Science 5478, 771-775 (2009).
16
Multi-facet rating of product reviews. (English)
Boughanem, Mohand (ed.) et al., Advances in information retrieval. 31th European conference on IR research, ECIR 2009, Toulouse, France, April 6‒9, 2009. Proceedings. Berlin: Springer (ISBN 978-3-642-00957-0/pbk). Lecture Notes in Computer Science 5478, 461-472 (2009).
17
Active learning strategies for multi-label text classification. (English)
Boughanem, Mohand (ed.) et al., Advances in information retrieval. 31th European conference on IR research, ECIR 2009, Toulouse, France, April 6‒9, 2009. Proceedings. Berlin: Springer (ISBN 978-3-642-00957-0/pbk). Lecture Notes in Computer Science 5478, 102-113 (2009).
18
Multi-faceted rating of product reviews (English)
ERCIM News 2009, No. 77 (2009).
19
Training data cleaning for text classification (English)
ICTIR, 29-41 (2009).
20
Result 1 to 20 of 43 total
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