W02-1706 we combined further use of the lemmatiser with the use of lexical resources
S01-1029 part-of-speech ( PoS ) tagger , and a lemmatiser . The tokeniser comprises a small
W06-1208 and modifier , using the morph lemmatiser . Next , we examine the BNC for
D08-1047 these words . We trained CST 's lemmatiser for each dataset to obtain flex
S12-1031 lemmatise using WordNet 's native lemmatiser . This yields a leaf-level synset
P13-1069 This solution allows to use our lemmatiser directly against chunker output
D08-1047 Jongejan ( 2006 ) presented a lemmatiser based on suffix rules . Although
W06-1208 participle ( + ed ) , using the morph lemmatiser ( Minnen et al. , 2001 ) . We
P05-1076 includes a tokenizer , tagger , lemmatiser , and a wide-coverage unification-based
W09-0705 illustrated by showing how an existing lemmatiser for Setswana can be improved
W04-1907 Minnen et al. 's ( 2000 ) morpha lemmatiser . As morpha is not XML - aware
W04-1007 Minnen et al. 's ( 2000 ) morpha lemmatiser . This program is not XML-aware
P13-1069 fails . A rudimentary analysis of lemmatiser output indicates that the most
M98-1021 annotation tools # 28a tokeniser , a lemmatiser , a tagger , etc. # 29 as well
W06-0701 markup is added using the morpha lemmatiser ( Minnen et al. , 2000 ) and
S01-1029 into parse probabilities . The lemmatiser ( IVIinnen et al. , 2001 ) reduces
P06-2001 parser uses the tokeniser , the lemmatiser , the chunker and the morphosyntactic
E03-2013 distribution ) , is a rule-based lemmatiser that produces an affix and root
P13-1069 the entire system ( chunker + NP lemmatiser ) on the whole test set and performance
W02-1706 Minnen et al. 's ( 2000 ) morpha lemmatiser : the PCDATA content of each
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