| 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,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. |
| 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. |
| 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. |
| 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. |
| lr,1-3-P03-1050,bq | training resources </term> . No <term> parallel | text | </term> is needed after the <term> training | #4478 No parallel text is needed after the training phase. |
| 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,20-2-P01-1008,bq | translations </term> of the same <term> source | text | </term> . Our approach yields <term> phrasal | #1798 We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. |
| 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,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. |
| 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. |
| 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,31-1-H01-1040,bq | - can be used to enhance access to <term> | text | collections </term> via a standard <term> text | #305 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access totext collections via a standard text browser. |
