|
<term>
sentences
</term>
. In this paper , we
|
present
|
<term>
SPoT
</term>
, a
<term>
sentence planner
|
#1340
In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. |
|
methods to collect
<term>
paraphrases
</term>
. We
|
present
|
an
<term>
unsupervised learning algorithm
|
#1778
We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. |
|
warrant serious
<term>
attention
</term>
, yet
|
present
|
<term>
natural language search engines
</term>
|
#1859
These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them. |
|
adopting
<term>
statistical techniques
</term>
. We
|
present
|
our
<term>
multi-level answer resolution
|
#2373
We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels. |
|
precision metric
</term>
. In this paper we
|
present
|
<term>
ONTOSCORE
</term>
, a system for scoring
|
#2439
In this paper we present ONTOSCORE, a system for scoring sets of concepts on the basis of an ontology. |
|
more useful for
<term>
NLP tasks
</term>
. We
|
present
|
an implementation of the
<term>
model
</term>
|
#2745
We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text. |
|
</term>
from
<term>
printed text
</term>
. We
|
present
|
an application of
<term>
ambiguity packing
|
#2785
We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain of sentence condensation. |
|
constraint-based parser/generator
</term>
. We
|
present
|
a new
<term>
part-of-speech tagger
</term>
|
#2910
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
|
more complex mixtures of techniques . We
|
present
|
a
<term>
syntax-based constraint
</term>
for
|
#3229
We present a syntax-based constraint for word alignment, known as the cohesion constraint. |
|
answering session
</term>
. In this paper we
|
present
|
a novel , customizable
<term>
IE paradigm
|
#3715
In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. |
|
<term>
NP - and non-NP-antecedents
</term>
. We
|
present
|
a set of
<term>
features
</term>
designed for
|
#3999
We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. |
|
feedback
</term>
. Based on these results , we
|
present
|
an
<term>
ECA
</term>
that uses
<term>
verbal
|
#5093
Based on these results, we present an ECA that uses verbal and nonverbal grounding acts to update dialogue state. |
|
significant positive effect on both tasks . We
|
present
|
a new
<term>
HMM tagger
</term>
that exploits
|
#5497
We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. |
|
supervised case
</term>
. Along the way , we
|
present
|
the first comprehensive comparison of
<term>
|
#5531
Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora or lexicons. |
|
achieved by the
<term>
algorithms
</term>
, we
|
present
|
a method of
<term>
HMM training
</term>
that
|
#5578
Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable. |
|
<term>
meanings
</term>
. In this paper , we
|
present
|
a
<term>
corpus-based supervised word sense
|
#5984
In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) with linguistic information. |
|
lemmas
</term>
is smaller and more robust . We
|
present
|
a
<term>
text mining method
</term>
for finding
|
#6093
We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora. |
|
summary
</term>
coherent . In this paper , we
|
present
|
our work on the detection of
<term>
question-answer
|
#6262
In this paper, we present our work on the detection of question-answer pairs in an email conversation for the task of email summarization. |
|
<term>
question-answer pairing
</term>
. We
|
present
|
a framework for the fast computation of
|
#6309
We present a framework for the fast computation of lexical affinity models. |
|
BalkaNet
</term>
and
<term>
EuroWordNet
</term>
. We
|
present
|
<term>
Minimum Bayes-Risk ( MBR ) decoding
|
#6544
We present Minimum Bayes-Risk (MBR) decoding for statistical machine translation. |