other,7-2-P01-1070,bq |
</term>
, which are built from
<term>
shallow
|
linguistic
|
features
</term>
of
<term>
questions
</term>
|
#2151
These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent a user's informational goals. |
tech,4-2-N03-1026,bq |
Our
<term>
system
</term>
incorporates a
<term>
|
linguistic
|
parser/generator
</term>
for
<term>
LFG
</term>
|
#2812
Our system incorporates alinguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection. |
other,27-1-C04-1112,bq |
classification ( maximum entropy )
</term>
with
<term>
|
linguistic
|
information
</term>
. Instead of building
|
#6006
In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) withlinguistic information. |
other,12-3-N04-1022,bq |
that incorporate different levels of
<term>
|
linguistic
|
information
</term>
from
<term>
word strings
|
#6587
We describe a hierarchy of loss functions that incorporate different levels oflinguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences. |
|
power of
<term>
phrasal SMT
</term>
with the
|
linguistic
|
generality available in a
<term>
parser
</term>
|
#9304
We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser. |
other,12-3-P06-2059,bq |
certain
<term>
layout structures
</term>
and
<term>
|
linguistic
|
pattern
</term>
. By using them , we can
|
#11438
The idea behind our method is to utilize certain layout structures andlinguistic pattern. |
other,6-2-C86-1132,bq |
RAREAS
</term>
draws on several kinds of
<term>
|
linguistic
|
and non-linguistic knowledge
</term>
and
|
#13952
RAREAS draws on several kinds oflinguistic and non-linguistic knowledge and mirrors a forecaster's apparent tendency to ascribe less precise temporal adverbs to more remote meteorological events. |
other,25-2-J86-3001,bq |
<term>
utterances
</term>
( called the
<term>
|
linguistic
|
structure
</term>
) , a structure of
<term>
|
#14127
In this theory, discourse structure is composed of three separate but interrelated components: the structure of the sequence of utterances (called thelinguistic structure), a structure of purposes (called the intentional structure), and the state of focus of attention (called the attentional state). |
other,8-3-P86-1011,bq |
turn to a discussion comparing the
<term>
|
linguistic
|
expressiveness
</term>
of the two
<term>
formalisms
|
#14616
We then turn to a discussion comparing thelinguistic expressiveness of the two formalisms. |
other,11-1-P86-1038,bq |
of
<term>
features
</term>
to describe
<term>
|
linguistic
|
objects
</term>
. Although
<term>
computational
|
#14634
Unification-based grammar formalisms use structures containing sets of features to describelinguistic objects. |
other,21-2-C88-2160,bq |
use any concepts of the underlying
<term>
|
linguistic
|
theory
</term>
: it is a reformulation of
|
#15682
The explanation of an ambiguity or an error for the purposes of correction does not use any concepts of the underlyinglinguistic theory: it is a reformulation of the erroneous or ambiguous sentence. |
other,17-2-C88-2162,bq |
does not readily lend itself in the
<term>
|
linguistic
|
domain
</term>
. For another ,
<term>
linguistic
|
#15767
For one thing, learning methodology applicable in general domains does not readily lend itself in thelinguistic domain. |
tech,12-4-C90-3063,bq |
statistics
</term>
on the output of other
<term>
|
linguistic
|
tools
</term>
. An experiment was performed
|
#16667
The scheme was implemented by gathering statistics on the output of otherlinguistic tools. |
other,27-2-J90-3002,bq |
dictionary
</term>
on the basis of a
<term>
|
linguistic
|
theory
</term>
. If we want valuable
<term>
|
#17257
The basic goal in building that editor was to provide an adequate tool to help lexicologists produce a valid and coherent dictionary on the basis of alinguistic theory. |
other,20-1-H92-1026,bq |
, that takes advantage of detailed
<term>
|
linguistic
|
information
</term>
to resolve
<term>
ambiguity
|
#18913
We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailedlinguistic information to resolve ambiguity. |
tech,27-1-H92-1060,bq |
a
<term>
question
</term>
when a full
<term>
|
linguistic
|
analysis
</term>
fails . This
<term>
robust
|
#19360
This paper describes an extension to the MIT ATIS (Air Travel Information Service) system, which allows it to answer a question when a fulllinguistic analysis fails. |
other,5-3-A94-1011,bq |
</term>
. A novel method for adding
<term>
|
linguistic
|
annotation
</term>
to
<term>
corpora
</term>
|
#19950
A novel method for addinglinguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction with unsupervised structure finding methods to derive notions of noun group, verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit. |