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
other,10-2-A88-1001,bq heuristically-produced complete <term> sentences </term> in <term> text </term> or <term> text-to-speech form </term>
other,12-4-P06-1013,bq are derived automatically from <term> raw text </term> . Experiments using the <term> SemCor
other,24-1-A92-1027,bq specific information from <term> unrestricted texts </term> where many of the <term> words </term>
other,31-1-N03-1018,bq progressing from generation of <term> true text </term> through its transformation into the
other,13-1-P84-1078,bq system </term> designed to create <term> cohesive text </term> through the use of <term> lexical substitutions
other,35-1-I05-4010,bq numbering system </term> in the <term> legal text hierarchy </term> . Basic methodology and
other,24-4-I05-2014,bq systems </term> outputting <term> unsegmented texts </term> with , for instance , <term> statistical
tech,26-3-P04-2005,bq Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> . Our method takes
other,3-3-C94-1026,bq proposed . We postulate that <term> source texts </term> and <term> target texts </term> should
other,6-2-C88-1044,bq </term> . We examine a broad range of <term> texts </term> to show how the distribution of <term>
other,14-4-C92-4207,bq spatial constraints </term> from the <term> text </term> , and represent them as the <term>
papers in English , many systems to run off texts have been developed . In this paper , we
tech,24-2-H94-1084,bq </term> , which creates the data for a <term> text retrieval application </term> and the <term>
tech,25-1-H94-1084,bq <term> image understanding </term> with <term> text understanding </term> . Our <term> document
other,24-1-N03-4010,bq answering capability </term> on <term> free text </term> . The demonstration will focus on
other,13-2-N03-2003,bq data </term> can be supplemented with <term> text </term> from the <term> web </term> filtered
other,29-3-P84-1078,bq antecedence </term> of each element in the <term> text </term> to select the proper <term> substitutions
other,6-2-P82-1035,bq , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term>
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