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Hits 1 – 13 of 13

1
Learning to Detect Malicious URLs
In: http://www.cs.ucsd.edu/%7Esavage/papers/TIST11.pdf (2011)
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2
Transliteration for resource-scarce languages
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
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
In: http://users.ics.aalto.fi/krista/papers/creutz07acmtslp.pdf (2007)
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5
A survey of statistical machine translation
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
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
In: http://research.microsoft.com/~jfgao/paper/gao_nie_zhou.talip2006.rev.pdf (2006)
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8
Confidence Estimation for NLP Applications
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
In: http://research.microsoft.com/~jfgao/paper/fp353-gao.pdf (2006)
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10
Trainable News Broadcast Boundary Identification Using Feature Density
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
In: http://cvit.iiit.ac.in/papers/Yashaswi2015AProbabilistic.pdf
Abstract: We address the problem of image retrieval using textual queries. In particular, we focus on descriptive queries that can be either in the form of simple captions (e.g., “a brown cat sleeping on a sofa”), or even long descriptions with mul-tiple sentences. We present a probabilistic approach that seamlessly integrates visual and textual information for the task. It relies on linguistically and syntactically motivated mid-level textual patterns (or phrases) that are automati-cally extracted from available descriptions. At the time of retrieval, the given query is decomposed into such phrases, and images are ranked based on their joint relevance with these phrases. Experiments on two popular datasets (UIUC Pascal Sentence and IAPR-TC12 benchmark) demonstrate that our approach effectively retrieves semantically mean-ingful images, and outperforms baseline methods.
Keyword: Artificial Intelligence-Vision and Scene Understanding General Terms Algorithms; Categories and Subject Descriptors H.3.3 [Information Search and Retrieval; Descriptive Queries; Experimentation; I.2.10 [Computing Methodologies; Measurement Keywords Image Retrieval; Retrieval models; Statistical Models
URL: http://cvit.iiit.ac.in/papers/Yashaswi2015AProbabilistic.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.698.8623
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12
Transliteration for Resource Scarce Languages
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
In: http://www-speech.sri.com/cgi-bin/run-distill?papers/acm2007-morph-asr.ps.gz
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