other,20-2-P01-1008,bq translations </term> of the same <term> source text </term> . Our approach yields <term> phrasal
lr,0-4-P03-1050,bq phase </term> . <term> Monolingual , unannotated text </term> can be used to further improve the
aggregation system </term> using each author 's text as a coherent <term> corpus </term> . Our approach
other,12-4-P06-1013,bq are derived automatically from <term> raw text </term> . Experiments using the <term> SemCor
other,29-3-P84-1078,bq antecedence </term> of each element in the <term> text </term> to select the proper <term> substitutions
other,26-4-P04-2005,bq exploits the large amount of <term> Chinese text </term> available in <term> corpora </term> and
other,28-1-C86-1132,bq sublanguages </term> with <term> stereotyped text structure </term> . <term> RAREAS </term> draws
tech,6-1-P84-1078,bq describes <term> Paul </term> , a <term> computer text generation system </term> designed to create
tech,24-5-P04-2010,bq open-domain question answering </term> and <term> text summarisation </term> . In this paper , we
tech,38-3-H01-1040,bq increased potential of <term> IE-enhanced text browsers </term> . At MIT Lincoln Laboratory
other,17-1-A94-1026,bq conversion </term> needed to input the <term> text </term> . It is critical , therefore , for
lr,11-4-P04-2010,bq <term> pronouns </term> in <term> unannotated text </term> by using a fully automatic sequence
tech,8-1-C90-3072,bq have become an integral part of most <term> text processing software </term> . From different
other,31-1-N03-1018,bq progressing from generation of <term> true text </term> through its transformation into the
other,35-1-I05-4010,bq numbering system </term> in the <term> legal text hierarchy </term> . Basic methodology and
other,13-2-N03-2003,bq data </term> can be supplemented with <term> text </term> from the <term> web </term> filtered
lr,26-6-P03-1050,bq affix lists </term> , and <term> human annotated text </term> , in addition to an <term> unsupervised
tech,3-1-C04-1116,bq smaller and more robust . We present a <term> text mining method </term> for finding <term> synonymous
other,37-1-A92-1027,bq </term> are unknown and much of the <term> text </term> is irrelevant to the task . The <term>
other,13-1-P82-1035,bq under the assumption that the input <term> text </term> will be in reasonably neat form ,
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