| other,10-1-P01-1004,bq | we compare the relative effects of <term> | segment order | </term> , <term> segmentation </term> and <term> | #1471 In this paper, we compare the relative effects ofsegment order, segmentation and segment contiguity on the retrieval performance of a translation memory system. |
| tech,13-1-P01-1004,bq | effects of <term> segment order </term> , <term> | segmentation | </term> and <term> segment contiguity </term> | #1474 In this paper, we compare the relative effects of segment order,segmentation and segment contiguity on the retrieval performance of a translation memory system. |
| other,15-1-P01-1004,bq | </term> , <term> segmentation </term> and <term> | segment contiguity | </term> on the <term> retrieval performance | #1476 In this paper, we compare the relative effects of segment order, segmentation andsegment contiguity on the retrieval performance of a translation memory system. |
| measure(ment),19-1-P01-1004,bq | <term> segment contiguity </term> on the <term> | retrieval performance | </term> of a <term> translation memory system | #1480 In this paper, we compare the relative effects of segment order, segmentation and segment contiguity on theretrieval performance of a translation memory system. |
| tech,23-1-P01-1004,bq | <term> retrieval performance </term> of a <term> | translation memory system | </term> . We take a selection of both <term> | #1484 In this paper, we compare the relative effects of segment order, segmentation and segment contiguity on the retrieval performance of atranslation memory system. |
| tech,6-2-P01-1004,bq | </term> . We take a selection of both <term> | bag-of-words and segment order-sensitive string comparison methods | </term> , and run each over both <term> character | #1494 We take a selection of bothbag-of-words and segment order-sensitive string comparison methods, and run each over both character- and word-segmented data, in combination with a range of local segment contiguity models (in the form of N-grams). |
| lr,19-2-P01-1004,bq | methods </term> , and run each over both <term> | character - and word-segmented data | </term> , in combination with a range of <term> | #1507 We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over bothcharacter - and word-segmented data, in combination with a range of local segment contiguity models (in the form of N-grams). |
| model,31-2-P01-1004,bq | </term> , in combination with a range of <term> | local segment contiguity models | </term> ( in the form of <term> N-grams </term> | #1519 We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both character- and word-segmented data, in combination with a range oflocal segment contiguity models (in the form of N-grams). |
| model,40-2-P01-1004,bq | contiguity models </term> ( in the form of <term> | N-grams | </term> ) . Over two distinct <term> datasets | #1528 We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both character- and word-segmented data, in combination with a range of local segment contiguity models (in the form ofN-grams). |
| lr,3-3-P01-1004,bq | N-grams </term> ) . Over two distinct <term> | datasets | </term> , we find that <term> indexing </term> | #1534 Over two distinctdatasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models. |
| tech,8-3-P01-1004,bq | <term> datasets </term> , we find that <term> | indexing | </term> according to simple <term> character | #1539 Over two distinct datasets, we find thatindexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models. |
| model,12-3-P01-1004,bq | indexing </term> according to simple <term> | character bigrams | </term> produces a <term> retrieval accuracy | #1543 Over two distinct datasets, we find that indexing according to simplecharacter bigrams produces a retrieval accuracy superior to any of the tested word N-gram models. |
| measure(ment),16-3-P01-1004,bq | character bigrams </term> produces a <term> | retrieval accuracy | </term> superior to any of the tested <term> | #1547 Over two distinct datasets, we find that indexing according to simple character bigrams produces aretrieval accuracy superior to any of the tested word N-gram models. |
| model,24-3-P01-1004,bq | </term> superior to any of the tested <term> | word N-gram models | </term> . Further , in their optimum <term> | #1555 Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the testedword N-gram models. |
| other,5-4-P01-1004,bq | </term> . Further , in their optimum <term> | configuration | </term> , <term> bag-of-words methods </term> | #1564 Further,in their optimumconfiguration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. |
| tech,7-4-P01-1004,bq | optimum <term> configuration </term> , <term> | bag-of-words methods | </term> are shown to be equivalent to <term> | #1566 Further,in their optimum configuration,bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. |
| tech,15-4-P01-1004,bq | </term> are shown to be equivalent to <term> | segment order-sensitive methods | </term> in terms of <term> retrieval accuracy | #1574 Further,in their optimum configuration, bag-of-words methods are shown to be equivalent tosegment order-sensitive methods in terms of retrieval accuracy, but much faster. |
| measure(ment),21-4-P01-1004,bq | order-sensitive methods </term> in terms of <term> | retrieval accuracy | </term> , but much faster . We also provide | #1580 Further,in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms ofretrieval accuracy, but much faster. |
