P00-1063 statistical approaches on ATR ( Automatic Term Recognition ) have achieved good results
P03-2020 in experiments . The result of automatic term recognition for " ( natural language processing
P03-2020 recognition The second step , automatic term recognition ( ATR ) , extracts important
P03-2020 search engines . The second step , automatic term recognition , extracts important terms from
P03-2020 size is about 500 sentences . 2.2 Automatic term recognition The second step , automatic term
E06-1029 sentences around the seed . 2.2 Automatic Term Recognition The next step is to extract candidate
E95-1003 interest . Given that concerns in automatic term recognition are practical , rather than theoretical
C94-2167 Ananladou 1 INTRODUCTION The topic of automatic term recognition ( ATR ) is of great interest
P03-2020 three steps : compiling corpus , automatic term recognition ( ATR ) , and filtering . This
E06-1029 three steps : corpus collection , automatic term recognition ( ATR ) , and filtering . 2.1
E97-1065 embedded to the C-value approach for automatic term recognition ( ATR ) , in the form of weights
I05-4001 computers . Therefore , what the automatic term recognition system find can only be taken
C02-1083 biology . Our system integrates automatic term recognition , term variation management ,
C02-1083 briefly present the quality of automatic term recognition and similarity measure calculation
C94-2167 relevance of other disciplines to automatic term recognition , such as Information Science
C04-1087 </figurecaption> <title> Enhancing automatic term recognition through recognition of variation
J07-1007 and Goran Nenadic , focuses on automatic term recognition ( ATR ) and Book Reviews automatic
C04-1087 NLM , 2004 ) ) . The task of an automatic term recognition ( ATR ) system is not only to
H05-1106 cation system . For multi-word automatic term recognition ( ATR ) , the C-value approach
P03-2020 certain domain has been studied as automatic term recognition ( Kageura and Umino , 1996 ;
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