| tech,4-1-N03-1033,ak | parser/generator </term> . We present a new <term> | part-of-speech tagger | </term> that demonstrates the following ideas | #2914 We present a newpart-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| other,22-1-N03-1033,ak | use of both preceding and following <term> | tag contexts | </term> via a <term> dependency network representation | #2932 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and followingtag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| model,26-1-N03-1033,ak | following <term> tag contexts </term> via a <term> | dependency network representation | </term> , ( ii ) broad use of <term> lexical | #2936 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via adependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| other,36-1-N03-1033,ak | representation </term> , ( ii ) broad use of <term> | lexical features | </term> , including jointly conditioning | #2946 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use oflexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| other,53-1-N03-1033,ak | consecutive words , ( iii ) effective use of <term> | priors | </term> in <term> conditional loglinear models | #2963 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use ofpriors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| model,55-1-N03-1033,ak | effective use of <term> priors </term> in <term> | conditional loglinear models | </term> , and ( iv ) fine-grained modeling | #2965 We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors inconditional loglinear models, and (iv) fine-grained modeling of unknown word features. | |
| tech,7-2-N03-1033,ak | these ideas together , the resulting <term> | tagger | </term> gives a 97.24 % <term> accuracy </term> | #2987 Using these ideas together, the resultingtagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. | |
| measure(ment),12-2-N03-1033,ak | <term> tagger </term> gives a 97.24 % <term> | accuracy | </term> on the <term> Penn Treebank WSJ </term> | #2992 Using these ideas together, the resulting tagger gives a 97.24%accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. | |
| tool,15-2-N03-1033,ak | 97.24 % <term> accuracy </term> on the <term> | Penn Treebank WSJ | </term> , an <term> error reduction </term> of | #2995 Using these ideas together, the resulting tagger gives a 97.24% accuracy on thePenn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. | |
| other,20-2-N03-1033,ak | <term> Penn Treebank WSJ </term> , an <term> | error reduction | </term> of 4.4 % on the best previous single | #3000 Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, anerror reduction of 4.4% on the best previous single automatically learned tagging result. | |
| other,30-2-N03-1033,ak | 4.4 % on the best previous single <term> | automatically learned tagging result | </term> . Sources of <term> training data </term> | #3010 Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous singleautomatically learned tagging result. |
