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SUMSS: a wide-field radio imaging survey of the southern sky – II. The source catalogue
This paper is the second in a series describing the Sydney University Molonglo Sky Survey (SUMSS) being carried out at 843 MHz with the Molonglo Observatory Synthesis Telescope (MOST). The survey…
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Wide-Coverage Efficient Statistical Parsing with CCG and Log-Linear Models
TLDR
This article describes a number of log-linear parsing models for an automatically extracted lexicalized grammar and develops a new model and efficient parsing algorithm which exploits all derivations, including CCG's nonstandard derivations.
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Learning multilingual named entity recognition from Wikipedia
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Evaluating Entity Linking with Wikipedia
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From distributional to semantic similarity
TLDR
This dissertation describes how to extract contexts from a corpus of over 2 billion words and introduces a new context-weighted approximation algorithm with bounded complexity in context vector size that significantly reduces the system runtime with only a minor performance penalty.
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Linguistically Motivated Large-Scale NLP with C&C and Boxer
TLDR
An NLP system which is based on syntactic and semantic formalisms from theoretical linguistics, and which is used to analyse the entire Gigaword corpus in less than 5 days using only 18 processors, represents a break-through in NLP technology.
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Parsing the WSJ Using CCG and Log-Linear Models
TLDR
A parallel implementation of the L-BFGS optimisation algorithm is described, which runs on a Beowulf cluster allowing the complete Penn Treebank to be used for estimation and a new efficient parsing algorithm for CCG which maximises expected recall of dependencies is developed.
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Improvements in Automatic Thesaurus Extraction
TLDR
An approximation algorithm is proposed, based on canonical attributes and coarse- and fine-grained matching, that reduces the time complexity and execution time of thesaurus extraction with only a marginal performance penalty.
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The Importance of Supertagging for Wide-Coverage CCG Parsing
TLDR
This paper describes the role of supertagging in a wide-coverage CCG parser which uses a log-linear model to select an analysis and shows that large increases in speed can be obtained by tightly integrating the supertagger with the CCG grammar and parser.
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Named Entity Recognition in Wikipedia
TLDR
This first NER evaluation on a Wikipedia gold standard (WG) corpus finds that an automatic annotation of Wikipedia has high agreement with WG and, when used as training data, outperforms newswire models by up to 7.7%.
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