| aggregation system </term> using each author 's | text | as a coherent <term> corpus </term> . Our approach | #6133 This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author's text as a coherent corpus. | |
| </term> which can make a fair copy of not only | texts | but also graphs and tables indispensable | #12254 In this paper, we report a system FROFF which can make a fair copy of not only texts but also graphs and tables indispensable to our papers. | |
| papers in English , many systems to run off | texts | have been developed . In this paper , we | #12231 In order to meet the needs of a publication of papers in English, many systems to run off texts have been developed. | |
| lr,0-4-P03-1050,bq | phase </term> . <term> Monolingual , unannotated | text | </term> can be used to further improve the | #4489 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. |
| 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. |
| lr,11-4-P04-2010,bq | <term> pronouns </term> in <term> unannotated | text | </term> by using a fully automatic sequence | #7081 Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process. |
| 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. |
| 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. |
| 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. |
| 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,0-1-A94-1026,bq | language translation </term> . <term> Japanese | texts | </term> frequently suffer from the <term> homophone | #20367 Japanese texts frequently suffer from the homophone errors caused by the KANA-KANJI conversion needed to input the text. |
| 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,10-5-P82-1035,bq | to aid the understanding of <term> scruffy | texts | </term> has been incorporated into a working | #13110 This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages. |
| 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. |
| other,12-3-C92-4207,bq | </term> , which takes <term> natural language | texts | </term> and produces a <term> model </term> of | #18444 It is done by an experimental computer program SPRINT, which takes natural language texts and produces a model of the described world. |
| 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. |
| 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,13-2-N03-2003,bq | data </term> can be supplemented with <term> | text | </term> from the <term> web </term> filtered | #3041 In this paper, we show how training data can be supplemented withtext from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. |
| 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. |
