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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Industry watch Dale, Robert In: Natural language engineering. - Cambridge : Cambridge University Press 13 (2007) 3, 283-286 BLLDB Show details 2 Industry Watch: Is 2007 the Year of Question-Answering? Dale, Robert In: Natural language engineering. - Cambridge : Cambridge University Press 13 (2007) 1, 91 OLC Linguistik Show details 3 Handling conjunctions in named entities Mazur, Pawel; Dale, Robert In: Lingvisticae investigationes. - Amsterdam : Benjamins 30 (2007) 1, 49-68 BLLDB Show details 4 Handling conjunctions in named entities Dale, Robert; Mazur, Pawel. - : Berlin : Springer, 2007 BASE Show details 5 Handling conjunctions in named entities Mazur, Pawel; Dale, Robert. - : John Benjamins Publishing Company, 2007 BASE Show details 6 A supervised machine learning approach to conjunction disambiguation in named entities Mazur, Pawel; Dale, Robert. - : Hyderabad, India : IJCAI-07 Workshop on Analytics for Noisy Unstructured Text Data (AND-07), 2007 Abstract: Although the literature contains reports of very high accuracy figures for the recognition of named entities in text, there are still some named entity phenomena that remain problematic for existing text processing systems. One of these is the ambiguity of conjunctions in candidate named entity strings, an all-too-prevalent problem in corporate and legal documents. In this paper, we distinguish four uses of the conjunction in these strings, and explore the use of a supervised machine learning approach to conjunction disambiguation trained on a very limited set of 'name internal' features that avoids the need for expensive lexical or semantic resources. We achieve 84% correctly classified examples using k-fold evaluation on a data set of 600 instances. We argue that further improvements are likely to require the use of wider domain knowledge and name external features. ; 8 page(s) Keyword: 080100 Artificial Intelligence and Image Processing URL: http://hdl.handle.net/1959.14/1246493 BASE Hide details 7 GLEU : Automatic evaluation of sentence-level fluency Mutton, Andrew; Dras, Mark; Wan, Stephen. - : Stroudsburg, PA : Association for Computational Linguistics (ACL), 2007 BASE Show details 8 Global revision in summarisation : generating novel sentences with Prim's algorithm Wan, Stephen; Dale, Robert; Dras, Mark. - : Melbourne, Australia : Pacific Association for Computational Linguistics, 2007 BASE Show details 9 The Semantic representation of temporal expressions in text Dale, Robert; Mazur, Pawel. - : Berlin, 2007. : New York : Springer-Verlag, 2007 BASE Show details

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