| tech,11-2-H01-1041,bq | consists of two <term> core modules </term> , <term> | language | understanding and generation modules </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 | generation modules </term> mediated by a <term> | language | neutral meaning representation </term> called | #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 | of <term> Korean </term> ( a <term> verb final | language | </term> with <term> overt case markers </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 | order generation </term> of the <term> target | language | </term> . ( iii ) <term> Rapid system development | #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 <term> evaluation </term> of <term> human | language | learners </term> , to the <term> output </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 | provide information about both the <term> human | language | learning process </term> , the <term> translation | #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 | </term> of <term> MT output </term> . A <term> | language | learning experiment </term> showed that <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 | differentiate <term> native from non-native | language | essays </term> in less than 100 <term> words | #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 | sources </term> . We integrate a <term> spoken | language | understanding system </term> with <term> intelligent | #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 | extensively studied by the <term> natural | language | generation community </term> , though rarely | #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 | address the problem of combining several <term> | language | models ( LMs ) </term> . We find that simple | #1038 In this paper, we address the problem of combining severallanguage models (LMs). |
| tech,11-5-H01-1058,bq | clearly show the need for a <term> dynamic | language | model combination </term> to improve the <term> | #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 | key prediction </term> and <term> Thai-English | language | identification </term> . The paper also proposes | #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 | than 99 % <term> accuracy </term> in both <term> | language | identification </term> and <term> key prediction | #1287 Our algorithm reported more than 99% accuracy in bothlanguage identification and key prediction. |
| other,10-5-P01-1007,bq | of the <term> main parser </term> for a <term> | language | L </term> are directed by a <term> guide </term> | #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 | interpretation and generation of natural | language | </term> , current systems use manual or semi-automatic | #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 | attention </term> , yet present <term> natural | language | search engines </term> perform poorly on <term> | #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 | operational semantics </term> of <term> natural | language | applications </term> improve , even larger | #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 | training </term> modules of a <term> natural | language | generator </term> have recently been proposed | #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 | evaluated on three different <term> spoken | language | system domains </term> . Motivated by the | #2302 The classification accuracy of the method is evaluated on three different spoken language system domains. |
