Concordance

Query C04-1147 18 >
Sort Right 18 (839.2 per million)

other,11-5-C04-1147,bq	We apply it in combination with a <term> terabyte corpus </term> to answer <term> natural language tests </term> , achieving encouraging results .	#6428 We apply it in combination with a terabyte corpus to answernatural language tests, achieving encouraging results.
other,17-2-C04-1147,bq	The framework is composed of a novel algorithm to efficiently compute the <term> co-occurrence distribution </term> between pairs of <term> terms </term> , an <term> independence model </term> , and a <term> parametric affinity model </term> .	#6338 The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs ofterms, an independence model, and a parametric affinity model.
model,20-2-C04-1147,bq	The framework is composed of a novel algorithm to efficiently compute the <term> co-occurrence distribution </term> between pairs of <term> terms </term> , an <term> independence model </term> , and a <term> parametric affinity model </term> .	#6341 The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms, anindependence model, and a parametric affinity model.
model,22-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6372 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to createsequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.
model,4-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6354 In comparison with previousmodels, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.
model,25-2-C04-1147,bq	The framework is composed of a novel algorithm to efficiently compute the <term> co-occurrence distribution </term> between pairs of <term> terms </term> , an <term> independence model </term> , and a <term> parametric affinity model </term> .	#6346 The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms, an independence model, and aparametric affinity model.
model,9-1-C04-1147,bq	We present a framework for the fast computation of <term> lexical affinity models </term> .	#6317 We present a framework for the fast computation oflexical affinity models.
lr,50-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6400 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in thecorpus.
other,44-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6394 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words orphrases at any distance in the corpus.
other,12-2-C04-1147,bq	The framework is composed of a novel algorithm to efficiently compute the <term> co-occurrence distribution </term> between pairs of <term> terms </term> , an <term> independence model </term> , and a <term> parametric affinity model </term> .	#6333 The framework is composed of a novel algorithm to efficiently compute theco-occurrence distribution between pairs of terms, an independence model, and a parametric affinity model.
other,13-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6363 In comparison with previous models, which either use arbitrary windows to computesimilarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.
model,31-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6381 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus onmodels intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.
other,36-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6386 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture theco-occurrence patterns of any pair of words or phrases at any distance in the corpus.
other,42-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6392 In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair ofwords or phrases at any distance in the corpus.
other,15-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6365 In comparison with previous models, which either use arbitrary windows to compute similarity betweenwords or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.
lr,7-5-C04-1147,bq	We apply it in combination with a <term> terabyte corpus </term> to answer <term> natural language tests </term> , achieving encouraging results .	#6424 We apply it in combination with aterabyte corpus to answer natural language tests, achieving encouraging results.
other,10-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6360 In comparison with previous models, which either use arbitrarywindows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.
other,18-3-C04-1147,bq	In comparison with previous <term> models </term> , which either use arbitrary <term> windows </term> to compute <term> similarity </term> between <term> words </term> or use <term> lexical affinity </term> to create <term> sequential models </term> , in this paper we focus on <term> models </term> intended to capture the <term> co-occurrence patterns </term> of any pair of <term> words </term> or <term> phrases </term> at any distance in the <term> corpus </term> .	#6368 In comparison with previous models, which either use arbitrary windows to compute similarity between words or uselexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus.


	in Help