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» Evaluating learning algorithms and classifiers
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ACL
2001
13 years 10 months ago
Scaling to Very Very Large Corpora for Natural Language Disambiguation
The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be o...
Michele Banko, Eric Brill
LWA
2004
13 years 10 months ago
Modeling Rule Precision
This paper reports first results of an empirical study of the precision of classification rules on an independent test set. We generated a large number of rules using a general co...
Johannes Fürnkranz
TREC
2003
13 years 10 months ago
Methods for Accurate Retrieval of MEDLINE Citations in Functional Genomics
The lack of discipline and consistency in gene naming poses a formidable challenge to researchers in locating relevant information sources in the genomics literature. The research...
Mehmet Kayaalp, Alan R. Aronson, Susanne M. Humphr...
COLING
2000
13 years 10 months ago
Robust German Noun Chunking With a Probabilistic Context-Free Grammar
We present a noun chunker for German which is based on a head-lexicalised probabilistic contextfl'ee grammar. A manually developed grammar was semi-automatically extended wit...
Helmut Schmid, Sabine Schulte im Walde
MT
2007
100views more  MT 2007»
13 years 8 months ago
Semi-supervised model adaptation for statistical machine translation
Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in th...
Nicola Ueffing, Gholamreza Haffari, Anoop Sarkar