| tech,25-1-H92-1095,bq | document processing </term> , integrating <term> | language | understanding </term> with <term> speech recognition | #19662 Language understanding work at Paramax focuses on applying general-purpose language understanding technology to spoken language understanding, text understanding, and document processing, integratinglanguage understanding with speech recognition, knowledge-based information retrieval and image understanding. |
| other,12-1-A94-1017,bq | ( APs ) </term> for <term> real-time spoken | language | translation </term> . <term> Spoken language | #20207 This paper proposes a model using associative processors (APs) for real-time spoken language translation. |
| other,0-2-A94-1017,bq | language translation </term> . <term> Spoken | language | translation </term> requires ( 1 ) an accurate | #20211 Spoken language translation requires (1) an accurate translation and (2) a real-time response. |
| other,14-9-A94-1017,bq | meets the vital requirements of <term> spoken | language | translation </term> . <term> Japanese texts | #20363 Thus, our model, TDMT on APs, meets the vital requirements of spoken language translation. |
| other,15-6-C94-1026,bq | which are selected from different <term> | language | families </term> . In <term> optical character | #20610 Most importantly, the experimental objects are Chinese-English texts, which are selected from differentlanguage families. |
| other,10-1-C94-1030,bq | speech recognition </term> of a <term> natural | language | </term> , it has been difficult to detect | #20624 In optical character recognition and continuous speech recognition of a natural language, it has been difficult to detect error characters which are wrongly deleted and inserted. |
| tech,4-1-C94-1061,bq | <term> concurrent , object-oriented natural | language | parsing </term> is introduced . Complete <term> | #20821 A grammar model for concurrent, object-oriented natural language parsing is introduced. |
| model,6-1-H94-1014,bq | paper introduces a simple mixture <term> | language | model </term> that attempts to capture <term> | #21217 This paper introduces a simple mixturelanguage model that attempts to capture long distance constraints in a sentence or paragraph. |
