D09-1162 sentences is an open challenge in text mining . In this paper , we describe
D09-1157 by the community of biomedical text mining . Chen et al. ( 2005 ) collected
D09-1145 important for most biomedical text mining applications . We introduce an
C02-1008 model to further exploit anchor text mining for translating Web queries .
D09-1157 Ltd. . The UK National Centre for Text Mining is funded by JISC . The ITI-TXM
C04-1116 Nasukawa Abstract We present a text mining method for finding synonymous
C04-1071 applicable also to this kind of text mining task . We developed a high-precision
D12-1020 create the kind of rich output for text mining discussed in the introduction
A00-1004 paper we first describe a parallel text mining system that finds parallel texts
D09-1146 for a problem called comparative text mining ( CTM ) . Given news articles
D11-1141 , information extraction , and text mining over tweets . Not surprisingly
D08-1050 require processing for biomedical text mining . 3 Approach Our approach to
D08-1062 PPIs is important in biomedical text mining . 4.2 Experiment 1 : PPI sentence
C04-1117 such as information retrieval , text mining and information extraction .
A00-1004 implementation we used for parallel text mining , translation model training
A00-1004 describe the results of our parallel text mining and translation model training
C04-1150 ontology population method based on text mining and machine learning techniques
D11-1130 high-dimensional settings such as text mining ( Niu et al. , 2010 ) . For example
C04-1087 JISC-funded National Centre for Text Mining ( NaCTeM ) , Manchester , UK
D10-1122 database record deduplication , text mining , and information retrieval (
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