other,31-1-H01-1040,bq - can be used to enhance access to <term> text collections </term> via a standard <term> text
other,11-7-H01-1042,bq six extracts of <term> translated newswire text </term> . Some of the extracts were <term>
other,20-2-P01-1008,bq translations </term> of the same <term> source text </term> . Our approach yields <term> phrasal
other,31-1-N03-1018,bq progressing from generation of <term> true text </term> through its transformation into the
other,13-2-N03-2003,bq data </term> can be supplemented with <term> text </term> from the <term> web </term> filtered
other,24-1-N03-4010,bq answering capability </term> on <term> free text </term> . The demonstration will focus on
lr,1-3-P03-1050,bq training resources </term> . No <term> parallel text </term> is needed after the <term> training
tech,3-1-C04-1116,bq smaller and more robust . We present a <term> text mining method </term> for finding <term> synonymous
tech,26-3-P04-2005,bq Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> . Our method takes
lr,11-4-P04-2010,bq <term> pronouns </term> in <term> unannotated text </term> by using a fully automatic sequence
other,24-4-I05-2014,bq systems </term> outputting <term> unsegmented texts </term> with , for instance , <term> statistical
other,35-1-I05-4010,bq numbering system </term> in the <term> legal text hierarchy </term> . Basic methodology and
tech,15-2-N06-4001,bq researchers who are not experts in <term> text mining </term> . As evidence of its usefulness
other,12-4-P06-1013,bq are derived automatically from <term> raw text </term> . Experiments using the <term> SemCor
papers in English , many systems to run off texts have been developed . In this paper , we
other,13-1-P82-1035,bq under the assumption that the input <term> text </term> will be in reasonably neat form ,
tech,6-1-P84-1078,bq describes <term> Paul </term> , a <term> computer text generation system </term> designed to create
other,28-1-C86-1132,bq sublanguages </term> with <term> stereotyped text structure </term> . <term> RAREAS </term> draws
other,10-2-A88-1001,bq heuristically-produced complete <term> sentences </term> in <term> text </term> or <term> text-to-speech form </term>
other,6-2-C88-1044,bq </term> . We examine a broad range of <term> texts </term> to show how the distribution of <term>
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