|
carries important information yet it is
|
also
|
time consuming to document . Given the
|
#11
Oral communication is ubiquitous and carries important information yet it is also time consuming to document. |
|
<term>
industry watch
</term>
function . We
|
also
|
report results of a preliminary ,
<term>
|
#345
We also report results of a preliminary, qualitative user evaluation of the system, which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers. |
|
database
</term>
.
<term>
Requestors
</term>
can
|
also
|
instruct the
<term>
system
</term>
to notify
|
#866
Requestors can also instruct the system to notify them when the status of a request changes or when a request is complete. |
|
language identification
</term>
. The paper
|
also
|
proposes
<term>
rule-reduction algorithm
</term>
|
#1264
The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules. |
|
retrieval accuracy
</term>
, but much faster . We
|
also
|
provide evidence that our findings are
|
#1588
We also provide evidence that our findings are scalable. |
|
to perform an exhaustive comparison , we
|
also
|
evaluate a
<term>
hand-crafted template-based
|
#2080
In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. |
|
target
<term>
recognition task
</term>
, but
|
also
|
that it is possible to get bigger performance
|
#3059
In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. |
|
predicate-argument structures
</term>
. We
|
also
|
introduce a new way of automatically identifying
|
#3730
We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. |
|
<term>
sense coverage
</term>
. Our analysis
|
also
|
highlights the importance of the issue
|
#4917
Our analysis also highlights the importance of the issue of domain dependence in evaluating WSD programs. |
|
</term>
than that of
<term>
LTAG
</term>
. We
|
also
|
investigate the reason for that difference
|
#5141
We also investigate the reason for that difference. |
|
source-channel transliteration model
</term>
,
|
also
|
called
<term>
n-gram transliteration model
|
#5783
Under this framework, a joint source-channel transliteration model, also called n-gram transliteration model (ngram TM), is further proposed to model the transliteration process. |
|
extensive system development effort but
|
also
|
improves the
<term>
transliteration accuracy
|
#5835
Our study reveals that the proposed method not only reduces an extensive system development effort but also improves the transliteration accuracy significantly. |
|
English
</term>
and
<term>
Chinese
</term>
, and
|
also
|
exploits the large amount of
<term>
Chinese
|
#6978
Our method takes advantage of the different way in which word senses are lexicalised in English and Chinese, and also exploits the large amount of Chinese text available in corpora and on the Web. |
|
<term>
statistical translation model
</term>
is
|
also
|
presented that deals such
<term>
phrases
</term>
|
#7375
A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. |
|
of
<term>
CMU 's SMT system
</term>
. It has
|
also
|
successfully been coupled with
<term>
rule-based
|
#8170
It has also successfully been coupled with rule-based and example based machine translation modules to build a multi engine machine translation system. |
|
research
</term>
. This piece of work has
|
also
|
laid a foundation for exploring and harvesting
|
#8298
This piece of work has also laid a foundation for exploring and harvesting English-Chinese bitexts in a larger volume from the Web. |
|
equivalence
</term>
and
<term>
entailment
</term>
. We
|
also
|
introduce a novel
<term>
classification method
|
#8368
We also introduce a novel classification method based on PER which leverages part of speech information of the words contributing to the word matches and non-matches in the sentence. |
|
model ’s score
</term>
of 88.2 % . The article
|
also
|
introduces a new
<term>
algorithm
</term>
for
|
#8863
The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. |
|
statistical machine translation system
</term>
. We
|
also
|
show that a good-quality
<term>
MT system
|
#9069
We also show that a good-quality MT system can be built from scratch by starting with a very small parallel corpus (100,000 words) and exploiting a large non-parallel corpus. |
|
machine translation task
</term>
, which can
|
also
|
be viewed as a
<term>
stochastic tree-to-tree
|
#9489
Second, we describe the graphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. |