Concordance

Query E06-1018 15 (699.3 per million)

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


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