Concordance

Query text 38 >
Filter all but first hit in document 24 (1,118.9 per million)

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.
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,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,31-1-N03-1018,bq	progressing from generation of <term> true	text	</term> through its transformation into the	#2699 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system.
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,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.
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.
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.
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.
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,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.
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.
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,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,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.
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,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,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.


	in Help