| other,24-2-C92-2115,bq | to make decisions on the basis of <term> | translation examples | </term> at appropriate pints in <term> MT. | #18102 Currently some attempts are being made to use case-based reasoning in machine translation, that is, to make decisions on the basis oftranslation examples at appropriate pints in MT. |
| other,30-1-C92-2115,bq | inherently a conglomeration of individual <term> | lexical rules | </term> . Currently some attempts are being | #18075 The transfer phase in machine translation (MT) systems has been considered to be more complicated than analysis and generation, since it is inherently a conglomeration of individuallexical rules. |
| tech,1-1-C92-2115,bq | without algorithmic additions . The <term> | transfer phase | </term> in <term> machine translation ( MT | #18046 Thetransfer phase in machine translation (MT) systems has been considered to be more complicated than analysis and generation, since it is inherently a conglomeration of individual lexical rules. |
| tech,11-2-C92-2115,bq | <term> case-based reasoning </term> in <term> | machine translation | </term> , that is , to make decisions on | #18089 Currently some attempts are being made to use case-based reasoning inmachine translation, that is, to make decisions on the basis of translation examples at appropriate pints in MT. |
| tech,12-3-C92-2115,bq | <term> transfer system </term> , called a <term> | Similarity-driven Transfer System ( SimTran ) | </term> , for use in such <term> case-based | #18121 This paper proposes a new type of transfer system, called aSimilarity-driven Transfer System ( SimTran ), for use in such case-based MT (CBMT). |
| tech,18-1-C92-2115,bq | considered to be more complicated than <term> | analysis | </term> and <term> generation </term> , since | #18063 The transfer phase in machine translation (MT) systems has been considered to be more complicated thananalysis and generation, since it is inherently a conglomeration of individual lexical rules. |
| tech,20-1-C92-2115,bq | complicated than <term> analysis </term> and <term> | generation | </term> , since it is inherently a conglomeration | #18065 The transfer phase in machine translation (MT) systems has been considered to be more complicated than analysis andgeneration, since it is inherently a conglomeration of individual lexical rules. |
| tech,23-3-C92-2115,bq | SimTran ) </term> , for use in such <term> | case-based MT ( CBMT ) | </term> . This paper introduces a robust <term> | #18132 This paper proposes a new type of transfer system, called a Similarity-driven Transfer System (SimTran), for use in suchcase-based MT ( CBMT ). |
| tech,30-2-C92-2115,bq | examples </term> at appropriate pints in <term> | MT. This paper proposes a new type of | </term><term> transfer system </term> , called | #18108 Currently some attempts are being made to use case-based reasoning in machine translation, that is, to make decisions on the basis of translation examples at appropriate pints inMT. This paper proposes a new type of transfer system, called a Similarity-driven Transfer System (SimTran), for use in such case-based MT (CBMT). |
| tech,4-1-C92-2115,bq | . The <term> transfer phase </term> in <term> | machine translation ( MT ) systems | </term> has been considered to be more complicated | #18049 The transfer phase inmachine translation ( MT ) systems has been considered to be more complicated than analysis and generation, since it is inherently a conglomeration of individual lexical rules. |
| tech,7-3-C92-2115,bq | paper proposes a new type of </term><term> | transfer system | </term> , called a <term> Similarity-driven | #18116 This paper proposes a new type oftransfer system, called a Similarity-driven Transfer System (SimTran), for use in such case-based MT (CBMT). |
| tech,8-2-C92-2115,bq | some attempts are being made to use <term> | case-based reasoning | </term> in <term> machine translation </term> | #18086 Currently some attempts are being made to usecase-based reasoning in machine translation, that is, to make decisions on the basis of translation examples at appropriate pints in MT. |
