other,24-4-I05-2014,bq The use of <term> BLEU </term> at the <term> character </term> level eliminates the <term> word segmentation problem </term> : it makes it possible to directly compare commercial <term> systems </term> outputting <term> unsegmented texts </term> with , for instance , <term> statistical MT systems </term> which usually segment their <term> outputs </term> .
other,35-1-I05-4010,bq In this paper we present our recent work on harvesting <term> English-Chinese bitexts </term> of the laws of Hong Kong from the <term> Web </term> and aligning them to the <term> subparagraph </term> level via utilizing the <term> numbering system </term> in the <term> legal text hierarchy </term> .
lr,20-3-I05-4010,bq The resultant <term> bilingual corpus </term> , 10.4 M <term> English words </term> and 18.3 M <term> Chinese characters </term> , is an authoritative and comprehensive <term> text collection </term> covering the specific and special domain of HK laws .
tech,15-2-N06-4001,bq <term> InfoMagnets </term> aims at making <term> exploratory corpus analysis </term> accessible to researchers who are not experts in <term> text mining </term> .
other,12-4-P06-1013,bq Our <term> combination methods </term> rely on <term> predominant senses </term> which are derived automatically from <term> raw text </term> .
In order to meet the needs of a publication of papers in English , many systems to run off texts have been developed .
In this paper , we report a system <term> FROFF </term> which can make a fair copy of not only texts but also graphs and tables indispensable to our papers .
other,13-1-P82-1035,bq Most large <term> text-understanding systems </term> have been designed under the assumption that the input <term> text </term> will be in reasonably neat form , e.g. , <term> newspaper stories </term> and other <term> edited texts </term> .
other,27-1-P82-1035,bq Most large <term> text-understanding systems </term> have been designed under the assumption that the input <term> text </term> will be in reasonably neat form , e.g. , <term> newspaper stories </term> and other <term> edited texts </term> .
other,6-2-P82-1035,bq However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .
other,26-2-P82-1035,bq However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .
other,10-5-P82-1035,bq This method of using <term> expectations </term> to aid the understanding of <term> scruffy texts </term> has been incorporated into a working <term> computer program </term> called <term> NOMAD </term> , which understands <term> scruffy texts </term> in the domain of Navy messages .
other,25-5-P82-1035,bq This method of using <term> expectations </term> to aid the understanding of <term> scruffy texts </term> has been incorporated into a working <term> computer program </term> called <term> NOMAD </term> , which understands <term> scruffy texts </term> in the domain of Navy messages .
tech,6-1-P84-1078,bq This report describes <term> Paul </term> , a <term> computer text generation system </term> designed to create <term> cohesive text </term> through the use of <term> lexical substitutions </term> .
other,13-1-P84-1078,bq This report describes <term> Paul </term> , a <term> computer text generation system </term> designed to create <term> cohesive text </term> through the use of <term> lexical substitutions </term> .
other,29-3-P84-1078,bq The system identities a strength of <term> antecedence recovery </term> for each of the <term> lexical substitutions </term> , and matches them against the <term> strength of potential antecedence </term> of each element in the <term> text </term> to select the proper <term> substitutions </term> for these elements .
other,28-1-C86-1132,bq This paper describes a system ( <term> RAREAS </term> ) which synthesizes marine weather forecasts directly from <term> formatted weather data </term> . Such <term> synthesis </term> appears feasible in certain <term> natural sublanguages </term> with <term> stereotyped text structure </term> .
other,8-3-C86-1132,bq The approach can easily be adapted to synthesize <term> bilingual or multMingual texts </term> .
other,10-2-A88-1001,bq <term> Multimedia answers </term> include <term> videodisc images </term> and heuristically-produced complete <term> sentences </term> in <term> text </term> or <term> text-to-speech form </term> .
other,6-2-C88-1044,bq We examine a broad range of <term> texts </term> to show how the distribution of <term> demonstrative forms and functions </term> is <term> genre dependent </term> .
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