other,3-3-C88-2162,bq |
linguistic domain
</term>
. For another ,
<term>
|
linguistic
|
representation
</term>
used by
<term>
language
|
#15773
For another,linguistic representation used by language processing systems is not geared to learning. |
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,27-2-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,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. |
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. |
lr,30-3-J90-3002,bq |
ensure the validity of such complex
<term>
|
linguistic
|
databases
</term>
. Our most important task
|
#17290
If we want valuable lexicons and grammars to achieve complex natural language processing, we must provide very powerful tools to help create and ensure the validity of such complexlinguistic databases. |
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,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. |
tech,18-2-H92-1060,bq |
</term>
already in place for the full
<term>
|
linguistic
|
analysis component
</term>
. Robust
<term>
|
#19382
This robust parsing capability was achieved through minor extensions of pre-existing components already in place for the fulllinguistic analysis component. |
other,3-7-C88-2162,bq |
identified two tasks : First , how
<term>
|
linguistic
|
concepts
</term>
are acquired from
<term>
training
|
#15844
First, howlinguistic concepts are acquired from training examples and organized in a hierarchy; this task was discussed in previous papers [Zernik87]. |
other,4-4-C88-2162,bq |
learning
</term>
. We introduced a new
<term>
|
linguistic
|
representation
</term>
, the
<term>
Dynamic
|
#15790
We introduced a newlinguistic representation, the Dynamic Hierarchical Phrasal Lexicon (DHPL) [Zernik88], to facilitate language acquisition. |
other,16-8-C88-2162,bq |
hierarchy
</term>
is used in predicting new
<term>
|
linguistic
|
concepts
</term>
. Thus , a
<term>
program
</term>
|
#15884
Second, we show in this paper how a lexical hierarchy is used in predicting newlinguistic concepts. |
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,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. |
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,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. |
other,1-3-J86-3001,bq |
<term>
attentional state
</term>
) . The
<term>
|
linguistic
|
structure
</term>
consists of segments of
|
#14157
Thelinguistic structure consists of segments of the discourse into which the utterances naturally aggregate. |
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. |