| lr,20-3-I05-4010,bq | an authoritative and comprehensive <term> | text | collection </term> covering the specific | #8272 The resultant bilingual corpus, 10.4M English words and 18.3M Chinese characters, is an authoritative and comprehensivetext collection covering the specific and special domain of HK laws. |
| tech,15-2-N06-4001,bq | researchers who are not experts in <term> | text | mining </term> . As evidence of its usefulness | #10891 InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts intext mining. |
| other,12-4-P06-1013,bq | are derived automatically from <term> raw | text | </term> . Experiments using the <term> SemCor | #11022 Our combination methods rely on predominant senses which are derived automatically from raw text. |
| other,13-1-P82-1035,bq | under the assumption that the input <term> | text | </term> will be in reasonably neat form , | #12957 Most large text-understanding systems have been designed under the assumption that the inputtext will be in reasonably neat form, e.g., newspaper stories and other edited texts. |
| tech,6-1-P84-1078,bq | describes <term> Paul </term> , a <term> computer | text | generation system </term> designed to create | #13751 This report describes Paul, a computer text generation system designed to create cohesive text through the use of lexical substitutions. |
| other,13-1-P84-1078,bq | system </term> designed to create <term> cohesive | text | </term> through the use of <term> lexical substitutions | #13758 This report describes Paul, a computer text generation system designed to create cohesive text through the use of lexical substitutions. |
| other,29-3-P84-1078,bq | antecedence </term> of each element in the <term> | text | </term> to select the proper <term> substitutions | #13816 The system identities a strength of antecedence recovery for each of the lexical substitutions, and matches them against the strength of potential antecedence of each element in thetext to select the proper substitutions for these elements. |
| other,28-1-C86-1132,bq | sublanguages </term> with <term> stereotyped | text | structure </term> . <term> RAREAS </term> draws | #13943 This paper describes a system (RAREAS) which synthesizes marine weather forecasts directly from formatted weather data. Such synthesis appears feasible in certain natural sublanguages with stereotyped text structure. |
| other,10-2-A88-1001,bq | heuristically-produced complete <term> sentences </term> in <term> | text | </term> or <term> text-to-speech form </term> | #14892 Multimedia answers include videodisc images and heuristically-produced complete sentences intext or text-to-speech form. |
| tech,8-1-C90-3072,bq | have become an integral part of most <term> | text | processing software </term> . From different | #16730 Spelling-checkers have become an integral part of mosttext processing software. |
| other,37-1-A92-1027,bq | </term> are unknown and much of the <term> | text | </term> is irrelevant to the task . The <term> | #17580 We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of thetext is irrelevant to the task. |
| other,14-4-C92-4207,bq | spatial constraints </term> from the <term> | text | </term> , and represent them as the <term> | #18468 To reconstruct the model, the authors extract the qualitative spatial constraints from thetext, and represent them as the numerical constraints on the spatial attributes of the entities. |
| tech,17-1-H92-1095,bq | spoken language understanding </term> , <term> | text | understanding </term> , and <term> document | #19654 Language understanding work at Paramax focuses on applying general-purpose language understanding technology to spoken language understanding,text understanding, and document processing, integrating language understanding with speech recognition, knowledge-based information retrieval and image understanding. |
| other,17-1-A94-1026,bq | conversion </term> needed to input the <term> | text | </term> . It is critical , therefore , for | #20383 Japanese texts frequently suffer from the homophone errors caused by the KANA-KANJI conversion needed to input thetext. |
| lr-prod,15-3-H94-1014,bq | <term> word </term><term> Wall Street Journal | text | corpus </term> . Using the <term> BU recognition | #21260 The models were constructed using a 5K vocabulary and trained using a 76 million word Wall Street Journal text corpus. |
| tech,25-1-H94-1084,bq | <term> image understanding </term> with <term> | text | understanding </term> . Our <term> document | #21385 Because of the complexity of documents and the variety of applications which must be supported, document understanding requires the integration of image understanding withtext understanding. |
| tech,24-2-H94-1084,bq | </term> , which creates the data for a <term> | text | retrieval application </term> and the <term> | #21412 Our document understanding technology is implemented in a system called IDUS (Intelligent Document Understanding System), which creates the data for atext retrieval application and the automatic generation of hypertext links. |
| tech,21-3-H94-1084,bq | <term> integration </term> of <term> image and | text | understanding </term> . | #21446 This paper summarizes the areas of research during IDUS development where we have found the most benefit from the integration of image and text understanding. |
