P13-1010 |
graph-based approach for local
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coherence modeling
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. We evaluate our system on three
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P11-2022 |
grid representation for local
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coherence modeling
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. The grid abstracts away information
|
W12-2004 |
model of pronominal anaphora for
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coherence modeling
|
. In their imple - mentation
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P08-2011 |
literature and applying them to
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coherence modeling
|
. Our first model distinguishes
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P05-1018 |
that assess the merits of the
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coherence modeling
|
framework introduced above .
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P13-1010 |
help . <title> Graph-based Local
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Coherence Modeling
|
</title> Guinaudeau Abstract
|
N13-1101 |
Faculty Research Award . <title>
|
Coherence Modeling
|
for the Automated Assessment
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P13-1107 |
on anomaly analysis and textual
|
coherence modeling
|
. Acknowledgments Thanks to the
|
J08-1001 |
proposed discourse representation for
|
coherence modeling
|
. We have presented several classes
|
S13-1043 |
prominent approach to entity-based
|
coherence modeling
|
nowadays is the entity grid model
|
S13-1043 |
Arguments Improve Argument Linking and
|
Coherence Modeling
|
</title> Roth Abstract Implicit
|
W08-0127 |
task , widely used in discourse
|
coherence modeling
|
, can be adopted as a testbed
|
P13-1010 |
entity grid , a method for local
|
coherence modeling
|
that captures the distribution
|
P13-1010 |
these promising results on local
|
coherence modeling
|
make us believe that our graphbased
|
N13-1101 |
scoring system were examined for
|
coherence modeling
|
of spoken re - sponses . The
|
P11-1118 |
generative pronoun resolver for
|
coherence modeling
|
originates in Elsner and Charniak
|
S13-1043 |
Experiment 2 : Implicit arguments in
|
coherence modeling
|
In our second experiment , we
|
N13-1101 |
average scores 2 , 2.5 , and 3 . For
|
coherence modeling
|
, we again use the J48 decision
|
S13-1043 |
Other entity-based approaches to
|
coherence modeling
|
include the pronoun model by
|
P11-1100 |
; Lin et al. , parser helps in
|
coherence modeling
|
. 2010 ; Wang et al. , 2010 )
|