| tech,4-5-E06-1018,ak | of pairs . The combination with a <term> | two-step clustering process | </term> using <term> sentence co-occurrences | #11107 The combination with atwo-step clustering process using sentence co-occurrences as features allows for accurate results. | |
| other,11-5-E06-1018,ak | <term> sentence co-occurrences </term> as <term> | features | </term> allows for accurate results . Additionally | #11114 The combination with a two-step clustering process using sentence co-occurrences asfeatures allows for accurate results. | |
| lr,10-7-E06-1018,ak | and independency of a given biased <term> | gold standard | </term> it also enables <term> automatic parameter | #11159 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,ak | gold standard </term> it also enables <term> | automatic parameter optimization | </term> of the <term> WSI algorithm </term> . | #11164 Offering advantages like reproducability and independency of a given biased gold standard it also enablesautomatic parameter optimization of the WSI algorithm. | |
| measure(ment),6-6-E06-1018,ak | Additionally , a novel and likewise <term> | automatic and unsupervised evaluation method | </term> inspired by Schutze 's ( 1992 ) idea | #11126 Additionally, a novel and likewiseautomatic and unsupervised evaluation method inspired by Schutze's (1992) idea of evaluation of word sense disambiguation algorithms is employed. | |
| other,8-3-E06-1018,ak | utilizes <term> clustering </term> of <term> | word co-occurrences | </term> . This approach differs from other | #11070 Like most existing approaches it utilizes clustering ofword co-occurrences. | |
| tech,22-6-E06-1018,ak | 's ( 1992 ) idea of evaluation of <term> | word sense disambiguation algorithms | </term> is employed . Offering advantages | #11142 Additionally, a novel and likewise automatic and unsupervised evaluation method inspired by Schutze's (1992) idea of evaluation ofword sense disambiguation algorithms is employed. | |
| other,16-4-E06-1018,ak | that it enhances the effect of the <term> | one sense per collocation observation | </term> by using <term> triplets of words </term> | #11089 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,6-2-E06-1018,ak | represents an instantiation of the <term> | one sense per collocation observation | </term> ( Gale et al. , 1992 ) . Like most | #11049 It represents an instantiation of theone sense per collocation observation (Gale et al., 1992). | |
| tech,20-7-E06-1018,ak | parameter optimization </term> of the <term> | WSI algorithm | </term> . We present results on <term> addressee | #11169 Offering advantages like reproducability and independency of a given biased gold standard it also enables automatic parameter optimization of theWSI algorithm. | |
| tech,7-4-E06-1018,ak | approach differs from other approaches to <term> | WSI | </term> in that it enhances the effect of | #11080 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,7-1-E06-1018,ak | In this paper a novel solution to <term> | automatic and unsupervised word sense induction ( WSI ) | </term> is introduced . It represents an | #11031 In this paper a novel solution toautomatic and unsupervised word sense induction ( WSI ) is introduced. | |
| other,23-4-E06-1018,ak | collocation observation </term> by using <term> | triplets of words | </term> instead of pairs . The combination | #11096 This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by usingtriplets of words instead of pairs. | |
| other,8-5-E06-1018,ak | two-step clustering process </term> using <term> | sentence co-occurrences | </term> as <term> features </term> allows for | #11111 The combination with a two-step clustering process usingsentence co-occurrences as features allows for accurate results. | |
| tech,6-3-E06-1018,ak | most existing approaches it utilizes <term> | clustering | </term> of <term> word co-occurrences </term> | #11068 Like most existing approaches it utilizesclustering of word co-occurrences. |
