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The Pennsylvania State University CiteSeerX Archives (13)
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Simple Search
Hits 1 – 13 of 13
1
Learning to Detect Malicious URLs
Justin Ma
;
Lawrence K. Saul
;
Stefan Savage
...
In: http://www.cs.ucsd.edu/%7Esavage/papers/TIST11.pdf (2011)
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2
Transliteration for resource-scarce languages
Manoj K. Chinnakotla
;
Om P. Damani
;
Avijit Satoskar
In: http://www.cse.iitb.ac.in/~damani/papers/TALIP10/transliterationTALIP10.pdf (2010)
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3
Beyond Blacklists: Learning to Detect Malicious Web Sites from Suspicious URLs
Justin Ma
;
Lawrence K. Saul
;
Stefan Savage
...
In: http://www.cs.ucsd.edu/~savage/papers/KDD09.pdf (2009)
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4
K.: Unsupervised models for morpheme segmentation and morphology learning
Mathias Creutz
;
Krista Lagus
In: http://users.ics.aalto.fi/krista/papers/creutz07acmtslp.pdf (2007)
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5
A survey of statistical machine translation
Adam Lopez
In: http://homepages.inf.ed.ac.uk/alopez/pdf/survey.pdf (2007)
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6
A study of statistical models for query translation: finding a good unit of translation
Jianfeng Gao
In: http://research.microsoft.com/~jfgao/paper/gao.nie.sigir06.camera.pdf (2006)
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7
Statistical query translation models for cross-language information retrieval
Jianfeng Gao
;
Jian-yun Nie
;
Ming Zhou
In: http://research.microsoft.com/~jfgao/paper/gao_nie_zhou.talip2006.rev.pdf (2006)
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8
Confidence Estimation for NLP Applications
Simona Gandrabur
;
George Foster
;
Guy Lapalme
In: http://iit-iti.nrc-cnrc.gc.ca/iit-publications-iti/docs/NRC-48755.pdf (2006)
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9
A study of statistical models for query translation: finding a good unit of translation
Jianfeng Gao
In: http://research.microsoft.com/~jfgao/paper/fp353-gao.pdf (2006)
Abstract:
This paper presents a study of three statistical query translation models that use different units of translation. We begin with a review of a word-based translation model that uses cooccurrence statistics for resolving translation ambiguities. The translation selection problem is then formulated under the framework of graphic model resorting to which the modeling assumptions and limitations of the co-occurrence model are discussed, and the research of finding better translation units is motivated. Then, two other models that use larger, linguistically motivated translation units (i.e., noun phrase and dependency triple) are presented. For each model, the modeling and training methods are described in detail. All query translation models are evaluated using TREC collections. Results show that larger translation units lead to more specific models that usually achieve better translation and cross-language information retrieval results.
Keyword:
Algorithms
;
Cross-Language Information Retrieval
;
Experimentation Keywords Query Translation
;
H.3.3 [Information Storage and Retrieval
;
Linguistic Structures
;
Retrieval models General Terms Design
;
Statistical Models
;
Theory
URL:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.146.3588
http://research.microsoft.com/~jfgao/paper/fp353-gao.pdf
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10
Trainable News Broadcast Boundary Identification Using Feature Density
Advay V. Mengle
;
David Palmer
In: http://web.mit.edu/advay/www/pub/siemens04-ref.pdf (2004)
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11
A Probabilistic Approach for Image Retrieval Using Descriptive Textual Queries
Yashaswi Verma
;
C. V. Jawahar
In: http://cvit.iiit.ac.in/papers/Yashaswi2015AProbabilistic.pdf
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12
Transliteration for Resource Scarce Languages
Manoj K. Chinnakotla
;
Om P. Damani
;
Avijit Satoskar
In: http://www.cse.iitb.ac.in/%7Edamani/papers/TALIP10/transliterationTALIP10.pdf
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13
Morph-Based Speech Recognition and Modeling of Out-of-Vocabulary Words Across Languages
Mathias Creutz
;
Teemu Hirsimäki
;
Mikko Kurimo
...
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/acm2007-morph-asr.ps.gz
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