|
understanding and generation modules
</term>
mediated
|
by
|
a
<term>
language neutral meaning representation
|
#428
The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame. |
|
users
</term>
has been extensively studied
|
by
|
the
<term>
natural language generation community
|
#979
The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems. |
|
<term>
word string
</term>
has been obtained
|
by
|
using a different
<term>
LM
</term>
. Actually
|
#1109
The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
|
the basis of
<term>
feedback
</term>
provided
|
by
|
<term>
human judges
</term>
. We reconceptualize
|
#1361
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. |
|
for a
<term>
language L
</term>
are directed
|
by
|
a
<term>
guide
</term>
which uses the
<term>
|
#1714
The non-deterministic parsing choices of the main parser for a language L are directed by a guide which uses the shared derivation forest output by a prior RCL parser for a suitable superset of L. |
|
engine
</term>
can be improved dramatically
|
by
|
incorporating an approximation of the
<term>
|
#1884
I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine's operational semantics. |
|
for a
<term>
spoken dialogue system
</term>
|
by
|
eliciting
<term>
subjective human judgments
|
#2065
In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue systemby eliciting subjective human judgments. |
|
language system domains
</term>
. Motivated
|
by
|
the success of
<term>
ensemble methods
</term>
|
#2307
Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora. |
|
performance gains from the
<term>
data
</term>
|
by
|
using
<term>
class-dependent interpolation
|
#3072
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 databy using class-dependent interpolation of N-grams. |
|
</term>
for learning
<term>
morphology
</term>
|
by
|
identifying
<term>
hubs
</term>
in an
<term>
|
#3162
We describe a simple unsupervised technique for learning morphologyby identifying hubs in an automaton. |
|
a
<term>
corpus
</term>
automatically tagged
|
by
|
the first
<term>
learner
</term>
. The resulting
|
#3371
Then, a Hidden Markov Model is trained on a corpus automatically tagged by the first learner. |
|
translingual reach into other
<term>
languages
</term>
|
by
|
leveraging
<term>
human language technology
|
#3629
It gives users the ability to spend their time finding more data relevant to their task, and gives them translingual reach into other languagesby leveraging human language technology. |
|
</term>
of
<term>
data objects
</term>
created
|
by
|
the
<term>
system
</term>
during each
<term>
|
#3702
The operation of the system will be explained in depth through browsing the repository of data objects created by the system during each question answering session. |
|
</term>
on both
<term>
systems
</term>
. Motivated
|
by
|
these arguments , we introduce a number
|
#4093
Motivated by these arguments, we introduce a number of new performance enhancing techniques including part of speech tagging, new similarity measures and expanded stop lists. |
|
<term>
models
</term>
are automatically derived
|
by
|
<term>
decision tree learning
</term>
using
|
#4358
Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by the system. |
|
further improve the
<term>
stemmer
</term>
|
by
|
allowing it to adapt to a desired
<term>
|
#4498
Monolingual, unannotated text can be used to further improve the stemmerby allowing it to adapt to a desired domain or genre. |
|
approximate
<term>
Arabic 's rich morphology
</term>
|
by
|
a
<term>
model
</term>
that a
<term>
word
</term>
|
#4606
We approximate Arabic's rich morphologyby a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
|
of
<term>
human agreement
</term>
. Motivated
|
by
|
this semantic criterion we analyze the
|
#5324
Motivated by this semantic criterion we analyze the empirical quality of distributional word feature vectors and its impact on word similarity results, proposing an objective measure for evaluating feature vector quality. |
|
</term>
, and evaluated their performance
|
by
|
means of two experiments : coarse-level
|
#5467
We tested the clustering and filtering processes on electronic newsgroup discussions, and evaluated their performance by means of two experiments: coarse-level clustering and simple information retrieval. |
|
<term>
accuracy
</term>
that can be achieved
|
by
|
the
<term>
algorithms
</term>
, we present
|
#5573
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. |