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	</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.
other,0-1-A94-1026,bq	language translation </term> . <term> Japanese	texts	</term> frequently suffer from the <term>	#20367 Japanese texts frequently suffer from the homophone errors caused by the KANA-KANJI conversion needed to input the text.
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,12-3-C92-4207,bq	</term> , which takes <term> natural language	texts	</term> and produces a <term> model </term>	#18444 It is done by an experimental computer program SPRINT, which takes natural language texts and produces a model of the described world.
other,2-1-C94-1026,bq	errors </term> . To align <term> bilingual	texts	</term> becomes a crucial issue recently	#20535 To align bilingual texts becomes a crucial issue recently.
other,24-1-A92-1027,bq	specific information from <term> unrestricted	texts	</term> where many of the <term> words </term>	#17568 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 the text is irrelevant to the task.
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,25-5-P82-1035,bq	</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.
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,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,3-3-C94-1026,bq	proposed . We postulate that <term> source	texts	</term> and <term> target texts </term> should	#20560 We postulate that source texts and target texts should share the same concepts, ideas, entities, and events.
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 of texts to show how the distribution of demonstrative forms and functions is genre dependent.
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,6-3-C94-1026,bq	<term> source texts </term> and <term> target	texts	</term> should share the same concepts ,	#20563 We postulate that source texts and target texts should share the same concepts, ideas, entities, and events.
other,7-6-C94-1026,bq	experimental objects are <term> Chinese-English	texts	</term> , which are selected from different	#20603 Most importantly, the experimental objects are Chinese-English texts , which are selected from different language families.
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 .


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