| Using only 40 <term> utterances </term> from the <term> target speaker </term> for <term> adaptation </term> , the <term> error rate </term> dropped to 4.1 % --- a 45 % reduction in <term> error </term> compared to the <term> SI </term> result . | #17202 Using only 40 utterances from the target speaker for adaptation, the error rate dropped to 4.1% --- a 45% reduction in error compared to the SI result. | |
| The <term> accuracy rate </term> of <term> syntactic disambiguation </term> is raised from 46.0 % to 60.62 % by using this novel approach . | #17937 The accuracy rate of syntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach. | |
| On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> . | #4897 On a subset of the most difficult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. | |
| In <term> head-to-head tests </term> against one of the best existing robust <term> probabilistic parsing models </term> , which we call <term> P-CFG </term> , the <term> HBG model </term> significantly outperforms <term> P-CFG </term> , increasing the <term> parsing accuracy </term> rate from 60 % to 75 % , a 37 % reduction in error . | #19041 In head-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. | |
| <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #4503 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. | |
| We then turn to a discussion comparing the <term> linguistic expressiveness </term> of the two <term> formalisms </term> . | #14611 We then turn to a discussion comparing the linguistic expressiveness of the two formalisms. | |
| This paper presents an <term> automatic scheme </term> for collecting <term> statistics </term> on <term> co-occurrence patterns </term> in a large <term> corpus </term> . To a large extent , these <term> statistics </term> reflect <term> semantic constraints </term> and thus are used to disambiguate <term> anaphora references </term> and <term> syntactic ambiguities </term> . | #16633 This paper presents an automatic scheme for collecting statistics on co-occurrence patterns in a large corpus. To a large extent, these statistics reflect semantic constraints and thus are used to disambiguate anaphora references and syntactic ambiguities. | |
| Our results show that <term> MT evaluation techniques </term> are able to produce useful <term> features </term> for <term> paraphrase classification </term> and to a lesser extent <term> entailment </term> . | #8414 Our results show that MT evaluation techniques are able to produce useful features for paraphrase classification and to a lesser extent entailment. | |
| We go , on to describe <term> FlexP </term> , a <term> bottom-up pattern-matching parser </term> that we have designed and implemented to provide these flexibilities for <term> restricted natural language </term> input to a limited-domain computer system . | #12783 We go, on to describe FlexP, a bottom-up pattern-matching parser that we have designed and implemented to provide these flexibilities for restricted natural language input to a limited-domain computer system. | |
| The request is passed to a <term> mobile , intelligent agent </term> for execution at the appropriate <term> database </term> . | #851 The request is passed to a mobile, intelligent agent for execution at the appropriate database. | |
| Our <term> logical definition </term> leads to a neat relation to <term> categorial grammar </term> , ( yielding a treatment of <term> Montague semantics </term> ) , a <term> parsing-as-deduction </term> in a <term> resource sensitive logic </term> , and a <term> learning algorithm </term> from <term> structured data </term> ( based on a <term> typing-algorithm </term> and <term> type-unification </term> ) . | #1950 Our logical definition leads to a neat relation to categorial grammar, (yielding a treatment of Montague semantics), a parsing-as-deduction in a resource sensitive logic, and a learning algorithm from structured data (based on a typing-algorithm and type-unification). | |
| <term> FERRET </term> utilizes a novel approach to <term> Q/A </term> known as <term> predictive questioning </term> which attempts to identify the <term> questions </term> ( and <term> answers </term> ) that <term> users </term> need by analyzing how a <term> user </term> interacts with a system while gathering information related to a particular scenario . | #11691 FERRET utilizes a novel approach to Q/A known as predictive questioning which attempts to identify the questions (and answers) that users need by analyzing how a user interacts with a system while gathering information related to a particular scenario. | |
| We describe how this information is used in a <term> prototype system </term> designed to support <term> information workers </term> ' access to a <term> pharmaceutical news archive </term> as part of their <term> industry watch </term> function . | #331 We describe how this information is used in a prototype system designed to support information workers' access to a pharmaceutical news archive as part of their industry watch function. | |
| In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> . | #2285 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier. | |
| It is argued that the method reduces <term> metaphor interpretation </term> from a <term> reconstruction </term> to a <term> recognition task </term> . | #12510 It is argued that the method reduces metaphor interpretation from a reconstructionto a recognition task. | |
| Experiments show that this approach is superior to a single <term> decision-tree classifier </term> . | #7063 Experiments show that this approach is superior to a single decision-tree classifier. | |
| Typically , information that makes it to a <term> summary </term> appears in many different <term> lexical-syntactic forms </term> in the input <term> documents </term> . | #7182 Typically, information that makes it to a summary appears in many different lexical-syntactic forms in the input documents. | |
| <term> Chat-80 </term> has been designed to be both efficient and easily adaptable to a variety of applications . | #12865 Chat-80 has been designed to be both efficient and easily adaptable to a variety of applications. | |
| If a <term> computer system </term> wishes to accept <term> natural language input </term> from its <term> users </term> on a routine basis , it must display a similar indifference . | #12718 If a computer system wishes to accept natural language input from its users on a routine basis, it must display a similar indifference. | |
| A standard <term> ATN </term> should be further developed in order to account for the <term> verbal interactions </term> of <term> task-oriented dialogs </term> . | #12430 A standard ATN should be further developed in order to account for the verbal interactions of task-oriented dialogs. |
