| model,3-1-H92-1026,bq | <term> accuracy </term> . We describe a <term> | generative probabilistic model of natural language | </term> , which we call <term> HBG </term> , | #18896 We describe agenerative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. |
| tech,13-1-H92-1026,bq | natural language </term> , which we call <term> | HBG | </term> , that takes advantage of detailed | #18906 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,bq | , that takes advantage of detailed <term> | linguistic information | </term> to resolve <term> ambiguity </term> . | #18913 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,bq | linguistic information </term> to resolve <term> | ambiguity | </term> . <term> HBG </term> incorporates <term> | #18917 We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolveambiguity. |
| tech,0-2-H92-1026,bq | </term> to resolve <term> ambiguity </term> . <term> | HBG | </term> incorporates <term> lexical , syntactic | #18919 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,bq | </term> . <term> HBG </term> incorporates <term> | lexical , syntactic , semantic , and structural information | </term> from the <term> parse tree </term> into | #18921 HBG incorporateslexical , syntactic , semantic , and structural information from the parse tree into the disambiguation process in a novel way. |
| other,13-2-H92-1026,bq | structural information </term> from the <term> | parse tree | </term> into the <term> disambiguation process | #18932 HBG incorporates lexical, syntactic, semantic, and structural information from theparse tree into the disambiguation process in a novel way. |
| tech,17-2-H92-1026,bq | the <term> parse tree </term> into the <term> | disambiguation process | </term> in a novel way . We use a <term> corpus | #18936 HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into thedisambiguation process in a novel way. |
| lr,3-3-H92-1026,bq | process </term> in a novel way . We use a <term> | corpus of bracketed sentences | </term> , called a <term> Treebank </term> , | #18946 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. |
| lr,10-3-H92-1026,bq | bracketed sentences </term> , called a <term> | Treebank | </term> , in combination with <term> decision | #18953 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,bq | Treebank </term> , in combination with <term> | decision tree building | </term> to tease out the relevant aspects | #18958 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,bq | tease out the relevant aspects of a <term> | parse tree | </term> that will determine the correct <term> | #18969 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,bq | </term> that will determine the correct <term> | parse | </term> of a <term> sentence </term> . This stands | #18976 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,bq | the correct <term> parse </term> of a <term> | sentence | </term> . This stands in contrast to the | #18979 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,10-4-H92-1026,bq | contrast to the usual approach of further <term> | grammar | </term> tailoring via the usual <term> linguistic | #18991 This stands in contrast to the usual approach of furthergrammar tailoring via the usual linguistic introspection in the hope of generating the correct parse. |
| other,15-4-H92-1026,bq | grammar </term> tailoring via the usual <term> | linguistic introspection | </term> in the hope of generating the correct | #18996 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,bq | the hope of generating the correct <term> | parse | </term> . In <term> head-to-head tests </term> | #19005 This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correctparse. |
| measure(ment),1-5-H92-1026,bq | the correct <term> parse </term> . In <term> | head-to-head tests | </term> against one of the best existing | #19008 Inhead-to-head tests against one of the best existing robust 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. |
| tech,10-5-H92-1026,bq | against one of the best existing robust <term> | probabilistic parsing models | </term> , which we call <term> P-CFG </term> | #19017 In head-to-head tests against one of the best existing robustprobabilistic 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. |
| tech,17-5-H92-1026,bq | parsing models </term> , which we call <term> | P-CFG | </term> , the <term> HBG model </term> significantly | #19024 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. |
