ACL
2012
12 years 3 months ago
2012
This paper presents a novel way of improving POS tagging on heterogeneous data. First, two separate models are trained (generalized and domain-specific) from the same data set by...
NOMS
2010
IEEE
13 years 11 months ago
2010
IEEE
—Application level traffic classification is one of the major issues in network monitoring and traffic engineering. In our previous study, we proposed a new traffic classificatio...
EMNLP
2004
14 years 2 months ago
2004
Multidocument extractive summarization relies on the concept of sentence centrality to identify the most important sentences in a document. Centrality is typically defined in term...
CLEF
2007
Springer
14 years 7 months ago
2007
Springer
We describe our participation in the WebCLEF 2007 task, targeted at snippet retrieval from web data. Our system ranks snippets based on a simple similarity-based centrality, inspir...
ICASSP
2008
IEEE
14 years 7 months ago
2008
IEEE
MMR (Maximum Marginal Relevance) is widely used in summarization for its simplicity and efficacy, and has been demonstrated to achieve comparable performance to other approaches ...
DEXAW
2008
IEEE
14 years 7 months ago
2008
IEEE
We consider topic detection without any prior knowledge of category structure or possible categories. Keywords are extracted and clustered based on different similarity measures u...
ACMSE
2009
ACM
14 years 7 months ago
2009
ACM
This paper describes a research effort to improve the use of the cosine similarity information retrieval technique to detect unknown, known or variances of known rogue software by...
WWW
2008
ACM
15 years 1 months ago
2008
ACM
Social tagging describes a community of users labeling web content with tags. It is a simple activity that enriches our knowledge about resources on the web. For a computer to hel...
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ICDE
2008
IEEE
15 years 2 months ago
2008
IEEE
We study the following problem: how to efficiently find in a collection of strings those similar to a given query string? Various similarity functions can be used, such as edit dis...