| tech,11-2-H01-1041,bq | The <term> CCLINC Korean-to-English translation system </term> consists of two <term> core modules </term> , <term> language understanding and generation modules </term> mediated by a <term> language neutral meaning representation </term> called a <term> semantic frame </term> . | #422 The CCLINC Korean-to-English translation system consists of two core modules,language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame. |
| tech,19-2-H01-1041,bq | The <term> CCLINC Korean-to-English translation system </term> consists of two <term> core modules </term> , <term> language understanding and generation modules </term> mediated by a <term> language neutral meaning representation </term> called a <term> semantic frame </term> . | #430 The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by alanguage neutral meaning representation called a semantic frame. |
| other,18-3-H01-1041,bq | The key features of the <term> system </term> include : ( i ) Robust efficient <term> parsing </term> of <term> Korean </term> ( a <term> verb final language </term> with <term> overt case markers </term> , relatively <term> free word order </term> , and frequent omissions of <term> arguments </term> ) . | #459 The key features of the system include: (i) Robust efficient parsing of Korean (a verb final language with overt case markers, relatively free word order, and frequent omissions of arguments). |
| other,17-4-H01-1041,bq | ( ii ) High quality <term> translation </term> via <term> word sense disambiguation </term> and accurate <term> word order generation </term> of the <term> target language </term> . | #495 (ii) High quality translation via word sense disambiguation and accurate word order generation of the target language. |
| other,22-1-H01-1042,bq | The purpose of this research is to test the efficacy of applying <term> automated evaluation techniques </term> , originally devised for the <term> evaluation </term> of <term> human language learners </term> , to the <term> output </term> of <term> machine translation ( MT ) systems </term> . | #567 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to the output of machine translation (MT) systems. |
| other,12-2-H01-1042,bq | We believe that these <term> evaluation techniques </term> will provide information about both the <term> human language learning process </term> , the <term> translation process </term> and the <term> development </term> of <term> machine translation systems </term> . | #594 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems. |
| other,1-4-H01-1042,bq | A <term> language learning experiment </term> showed that <term> assessors </term> can differentiate <term> native from non-native language essays </term> in less than 100 <term> words </term> . | #629 Alanguage learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. |
| other,9-4-H01-1042,bq | A <term> language learning experiment </term> showed that <term> assessors </term> can differentiate <term> native from non-native language essays </term> in less than 100 <term> words </term> . | #640 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. |
| tech,3-2-H01-1049,bq | We integrate a <term> spoken language understanding system </term> with <term> intelligent mobile agents </term> that mediate between <term> users </term> and <term> information sources </term> . | #799 We integrate a spoken language understanding system with intelligent mobile agents that mediate between users and information sources. |
| other,13-3-H01-1055,bq | The issue of <term> system response </term> to <term> users </term> has been extensively studied by the <term> natural language generation community </term> , though rarely in the context of <term> dialog systems </term> . | #982 The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems. |
| model,11-1-H01-1058,bq | In this paper , we address the problem of combining several <term> language models ( LMs ) </term> . | #1038 In this paper, we address the problem of combining severallanguage models (LMs). |
| tech,11-5-H01-1058,bq | We provide experimental results that clearly show the need for a <term> dynamic language model combination </term> to improve the <term> performance </term> further . | #1143 We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further. |
| tech,17-1-H01-1070,bq | This paper proposes a practical approach employing <term> n-gram models </term> and <term> error-correction rules </term> for <term> Thai key prediction </term> and <term> Thai-English language identification </term> . | #1259 This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification. |
| tech,10-3-H01-1070,bq | Our <term> algorithm </term> reported more than 99 % <term> accuracy </term> in both <term> language identification </term> and <term> key prediction </term> . | #1287 Our algorithm reported more than 99% accuracy in bothlanguage identification and key prediction. |
| other,10-5-P01-1007,bq | The <term> non-deterministic parsing choices </term> of the <term> main parser </term> for a <term> language L </term> are directed by a <term> guide </term> which uses the <term> shared derivation forest </term> output by a prior <term> RCL parser </term> for a suitable <term> superset of L. | #1710 The non-deterministic parsing choices of the main parser for alanguage L are directed by a guide which uses the shared derivation forest output by a prior RCL parser for a suitable superset of L. |
| tech,6-1-P01-1008,bq | While <term> paraphrasing </term> is critical both for <term> interpretation and generation of natural language </term> , current systems use manual or semi-automatic methods to collect <term> paraphrases </term> . | #1764 While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. |
| tech,14-2-P01-1009,bq | These <term> words </term> appear frequently enough in <term> dialog </term> to warrant serious <term> attention </term> , yet present <term> natural language search engines </term> perform poorly on <term> queries </term> containing them . | #1861 These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them. |
| tech,12-4-P01-1009,bq | The value of this approach is that as the <term> operational semantics </term> of <term> natural language applications </term> improve , even larger improvements are possible . | #1916 The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible. |
| tech,7-1-P01-1056,bq | <term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> . | #2020 Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches. |
| other,11-4-N03-1001,bq | The <term> classification accuracy </term> of the <term> method </term> is evaluated on three different <term> spoken language system domains </term> . | #2302 The classification accuracy of the method is evaluated on three different spoken language system domains. |
