Concordance

Query E06-1018 15 >
Multilevel Sort 15 (699.3 per million)

lr,10-7-E06-1018,bq	and independency of a given biased <term>	gold standard	</term> it also enables <term> automatic parameter	#10222 Offering advantages like reproducability and independency of a given biasedgold standard it also enables automatic parameter optimization of the WSI algorithm.
other,11-5-E06-1018,bq	<term> sentence co-occurrences </term> as <term>	features	</term> allows for accurate results . Additionally	#10177 The combination with a two-step clustering process using sentence co-occurrences asfeatures allows for accurate results.
other,16-4-E06-1018,bq	that it enhances the effect of the <term>	one sense per collocation observation	</term> by using triplets of <term> words </term>	#10152 This approach differs from other approaches to WSI in that it enhances the effect of theone sense per collocation observation by using triplets of words instead of pairs.
other,20-6-E06-1018,bq	inspired by Schutze 's ( 1992 ) idea of <term>	evaluation	</term> of <term> word sense disambiguation	#10203 Additionally, a novel and likewise automatic and unsupervised evaluation method inspired by Schutze's (1992) idea ofevaluation of word sense disambiguation algorithms is employed.
other,25-4-E06-1018,bq	observation </term> by using triplets of <term>	words	</term> instead of pairs . The combination	#10161 This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by using triplets ofwords instead of pairs.
other,6-2-E06-1018,bq	represents an instantiation of the <term>	one sense per collocation observation	</term> ( Gale et al. , 1992 ) . Like most	#10112 It represents an instantiation of theone sense per collocation observation (Gale et al., 1992).
other,8-5-E06-1018,bq	two-step clustering process </term> using <term>	sentence co-occurrences	</term> as <term> features </term> allows for	#10174 The combination with a two-step clustering process usingsentence co-occurrences as features allows for accurate results.
tech,15-7-E06-1018,bq	gold standard </term> it also enables <term>	automatic parameter optimization	</term> of the <term> WSI algorithm </term> .	#10227 Offering advantages like reproducability and independency of a given biased gold standard it also enablesautomatic parameter optimization of the WSI algorithm.
tech,20-7-E06-1018,bq	parameter optimization </term> of the <term>	WSI algorithm	</term> . We present results on <term> addressee	#10232 Offering advantages like reproducability and independency of a given biased gold standard it also enables automatic parameter optimization of theWSI algorithm.
tech,22-6-E06-1018,bq	) idea of <term> evaluation </term> of <term>	word sense disambiguation algorithms	</term> is employed . Offering advantages	#10205 Additionally, a novel and likewise automatic and unsupervised evaluation method inspired by Schutze's (1992) idea of evaluation ofword sense disambiguation algorithms is employed.
tech,4-5-E06-1018,bq	of pairs . The combination with a <term>	two-step clustering process	</term> using <term> sentence co-occurrences	#10170 The combination with atwo-step clustering process using sentence co-occurrences as features allows for accurate results.
tech,6-3-E06-1018,bq	most existing approaches it utilizes <term>	clustering of word co-occurrences	</term> . This approach differs from other	#10131 Like most existing approaches it utilizesclustering of word co-occurrences.
tech,7-4-E06-1018,bq	approach differs from other approaches to <term>	WSI	</term> in that it enhances the effect of	#10143 This approach differs from other approaches toWSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs.
tech,8-6-E06-1018,bq	a novel and likewise automatic and <term>	unsupervised evaluation method	</term> inspired by Schutze 's ( 1992 ) idea	#10191 Additionally, a novel and likewise automatic andunsupervised evaluation method inspired by Schutze's (1992) idea of evaluation of word sense disambiguation algorithms is employed.
tech,9-1-E06-1018,bq	a novel solution to automatic and <term>	unsupervised word sense induction ( WSI )	</term> is introduced . It represents an	#10096 In this paper a novel solution to automatic andunsupervised word sense induction ( WSI ) is introduced.


	in Help