| 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. |
| 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,3-1-C04-1116,bq | smaller and more robust . We present a <term> | text | mining method </term> for finding <term> synonymous | #6095 We present atext mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora. |
| tech,24-5-P04-2010,bq | open-domain question answering </term> and <term> | text | summarisation </term> . In this paper , we | #7122 Although the system performs well within a limited textual domain, further research is needed to make it effective for open-domain question answering andtext summarisation. |
| 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. |
| lr,26-6-P03-1050,bq | affix lists </term> , and <term> human annotated | text | </term> , in addition to an <term> unsupervised | #4560 Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. |
| other,26-4-P04-2005,bq | exploits the large amount of <term> Chinese | text | </term> available in <term> corpora </term> and | #6985 Our method takes advantage of the different way in which word senses are lexicalised in English and Chinese, and also exploits the large amount of Chinese text available in corpora and on the Web. |
| 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. |
| 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,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,24-1-N03-4010,bq | answering capability </term> on <term> free | text | </term> . The demonstration will focus on | #3660 The JAVELIN system integrates a flexible, planning-based architecture with a variety of language processing modules to provide an open-domain question answering capability on free text. |
| lr,19-2-N03-4010,bq | answer candidates </term> from the given <term> | text | corpus </term> . The operation of the <term> | #3681 The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the giventext corpus. |
| tech,38-3-H01-1040,bq | increased potential of <term> IE-enhanced | text | browsers </term> . At MIT Lincoln Laboratory | #383 We also report results of a preliminary, qualitative user evaluation of the system, which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers. |
| 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,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. |
| 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. |
| 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. |
| other,35-1-I05-4010,bq | numbering system </term> in the <term> legal | text | hierarchy </term> . Basic methodology and | #8239 In this paper we present our recent work on harvesting English-Chinese bitexts of the laws of Hong Kong from the Web and aligning them to the subparagraph level via utilizing the numbering system in the legal text hierarchy. |
| 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,11-7-H01-1042,bq | six extracts of <term> translated newswire | text | </term> . Some of the extracts were <term> | #695 Subjects were given a set of up to six extracts of translated newswire text. |
