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. |