Concordance

Query text 56 >
Subparts filter 56 (2,610.8 per million)

other,24-4-I05-2014,bq	systems </term> outputting <term> unsegmented	texts	</term> with , for instance , <term> statistical	#7771 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs.
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.
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.
	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.
	</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.
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,27-1-P82-1035,bq	newspaper stories </term> and other <term> edited	texts	</term> . However , a great deal of <term>	#12972 Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably neat form, e.g., newspaper stories and other edited texts.
other,6-2-P82-1035,bq	, a great deal of <term> natural language	texts	</term> e.g. , <term> memos </term> , rough <term>	#12982 However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neat texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc.
other,26-2-P82-1035,bq	that differ significantly from <term> neat	texts	</term> , posing special problems for readers	#13001 However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neat texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc.
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,25-5-P82-1035,bq	NOMAD </term> , which understands <term> scruffy	texts	</term> in the domain of Navy messages .	#13125 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.
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,8-3-C86-1132,bq	synthesize <term> bilingual or multMingual	texts	</term> . A method for <term> error correction	#13986 The approach can easily be adapted to synthesize bilingual or multMingual texts.
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,6-2-C88-1044,bq	</term> . We examine a broad range of <term>	texts	</term> to show how the distribution of <term>	#15199 We examine a broad range oftexts to show how the distribution of demonstrative forms and functions is genre dependent.


	in Help