| model,3-1-H92-1026,ak | OCR accuracy </term> . We describe a <term> | generative probabilistic model | </term> of <term> natural language </term> , | #23868 We describe agenerative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. | |
| other,7-1-H92-1026,ak | generative probabilistic model </term> of <term> | natural language | </term> , which we call <term> HBG </term> , | #23872 We describe a generative probabilistic model ofnatural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. | |
| model,13-1-H92-1026,ak | natural language </term> , which we call <term> | HBG | </term> , that takes advantage of detailed | #23878 We describe a generative probabilistic model of natural language, which we callHBG, that takes advantage of detailed linguistic information to resolve ambiguity. | |
| other,20-1-H92-1026,ak | , that takes advantage of detailed <term> | linguistic information | </term> to resolve <term> ambiguity </term> . | #23885 We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailedlinguistic information to resolve ambiguity. | |
| other,24-1-H92-1026,ak | linguistic information </term> to resolve <term> | ambiguity | </term> . <term> HBG </term> incorporates <term> | #23889 We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolveambiguity. | |
| model,0-2-H92-1026,ak | </term> to resolve <term> ambiguity </term> . <term> | HBG | </term> incorporates <term> lexical , syntactic | #23891 We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity.HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. | |
| other,2-2-H92-1026,ak | </term> . <term> HBG </term> incorporates <term> | lexical , syntactic , semantic , and structural information | </term> from the <term> parse tree </term> into | #23893 HBG incorporateslexical , syntactic , semantic , and structural information from the parse tree into the disambiguation process in a novel way. | |
| other,13-2-H92-1026,ak | structural information </term> from the <term> | parse tree | </term> into the <term> disambiguation process | #23904 HBG incorporates lexical, syntactic, semantic, and structural information from theparse tree into the disambiguation process in a novel way. | |
| other,17-2-H92-1026,ak | the <term> parse tree </term> into the <term> | disambiguation process | </term> in a novel way . We use a <term> corpus | #23908 HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into thedisambiguation process in a novel way. | |
| lr,3-3-H92-1026,ak | process </term> in a novel way . We use a <term> | corpus | </term> of bracketed <term> sentences </term> | #23918 We use acorpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. | |
| other,6-3-H92-1026,ak | use a <term> corpus </term> of bracketed <term> | sentences | </term> , called a <term> Treebank </term> , | #23921 We use a corpus of bracketedsentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. | |
| lr,10-3-H92-1026,ak | bracketed <term> sentences </term> , called a <term> | Treebank | </term> , in combination with <term> decision | #23925 We use a corpus of bracketed sentences, called aTreebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. | |
| tech,15-3-H92-1026,ak | Treebank </term> , in combination with <term> | decision tree building | </term> to tease out the relevant aspects | #23930 We use a corpus of bracketed sentences, called a Treebank, in combination withdecision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. | |
| other,26-3-H92-1026,ak | tease out the relevant aspects of a <term> | parse tree | </term> that will determine the correct <term> | #23941 We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of aparse tree that will determine the correct parse of a sentence. | |
| other,33-3-H92-1026,ak | </term> that will determine the correct <term> | parse | </term> of a <term> sentence </term> . This stands | #23948 We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correctparse of a sentence. | |
| other,36-3-H92-1026,ak | the correct <term> parse </term> of a <term> | sentence | </term> . This stands in contrast to the | #23951 We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of asentence. | |
| other,15-4-H92-1026,ak | further grammar tailoring via the usual <term> | linguistic introspection | </term> in the hope of generating the correct | #23968 This stands in contrast to the usual approach of further grammar tailoring via the usuallinguistic introspection in the hope of generating the correct parse. | |
| other,24-4-H92-1026,ak | the hope of generating the correct <term> | parse | </term> . In head-to-head tests against one | #23977 This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correctparse. | |
| model,9-5-H92-1026,ak | tests against one of the best existing <term> | robust probabilistic parsing models | </term> , which we call <term> P-CFG </term> | #23988 In head-to-head tests against one of the best existingrobust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error. | |
| model,17-5-H92-1026,ak | parsing models </term> , which we call <term> | P-CFG | </term> , the <term> HBG model </term> significantly | #23996 In head-to-head tests against one of the best existing robust probabilistic parsing models, which we callP-CFG, the HBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error. |
