other,6-2-P82-1035,bq |
</term>
. However , a great deal of
<term>
|
natural language texts
|
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
|
#12980
However, a great deal ofnatural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neat texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc. |
tool,21-5-P82-1035,bq |
<term>
computer program
</term>
called
<term>
|
NOMAD
|
</term>
, which understands
<term>
scruffy
|
#13120
This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program calledNOMAD, which understands scruffy texts in the domain of Navy messages. |
other,43-2-P82-1035,bq |
</term>
,
<term>
missing words
</term>
,
<term>
|
poor syntactic construction
|
</term>
,
<term>
missing periods
</term>
, etc
|
#13017
However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neat texts, posing special problems for readers, such as misspelled words, missing words,poor syntactic construction, missing periods, etc. |
other,21-3-P82-1035,bq |
<term>
surface English
</term>
and on
<term>
|
world knowledge
|
</term>
of the situation being described
|
#13047
Our solution to these problems is to make use of expectations, based both on knowledge of surface English and onworld knowledge of the situation being described. |
other,41-4-P82-1035,bq |
and resolve
<term>
referents
</term>
(
<term>
|
anaphora
|
</term>
) . This method of using
<term>
expectations
|
#13096
These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |
other,1-4-P82-1035,bq |
situation being described . These
<term>
|
syntactic and semantic expectations
|
</term>
can be used to figure out
<term>
unknown
|
#13056
Thesesyntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |
other,11-4-P82-1035,bq |
expectations
</term>
can be used to figure out
<term>
|
unknown words
|
</term>
from
<term>
context
</term>
, constrain
|
#13066
These syntactic and semantic expectations can be used to figure outunknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |
other,25-5-P82-1035,bq |
<term>
NOMAD
</term>
, which understands
<term>
|
scruffy texts
|
</term>
in the domain of Navy messages .
|
#13124
This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program called NOMAD, which understandsscruffy texts in the domain of Navy messages. |
other,10-5-P82-1035,bq |
</term>
to aid the understanding of
<term>
|
scruffy texts
|
</term>
has been incorporated into a working
|
#13109
This method of using expectations to aid the understanding ofscruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages. |
other,37-2-P82-1035,bq |
special problems for readers , such as
<term>
|
misspelled words
|
</term>
,
<term>
missing words
</term>
,
<term>
|
#13011
However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neat texts, posing special problems for readers, such asmisspelled words, missing words, poor syntactic construction, missing periods, etc. |
other,27-1-P82-1035,bq |
<term>
newspaper stories
</term>
and other
<term>
|
edited texts
|
</term>
. However , a great deal of
<term>
|
#12971
Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably neat form, e.g., newspaper stories and otheredited texts. |
other,34-4-P82-1035,bq |
fill in
<term>
missing words
</term>
(
<term>
|
elllpsis
|
</term>
) , and resolve
<term>
referents
</term>
|
#13089
These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |
other,19-4-P82-1035,bq |
context
</term>
, constrain the possible
<term>
|
word-senses
|
</term>
of
<term>
words with multiple meanings
|
#13074
These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possibleword-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |
other,23-1-P82-1035,bq |
be in reasonably neat form , e.g. ,
<term>
|
newspaper stories
|
</term>
and other
<term>
edited texts
</term>
|
#12967
Most large text-understanding systems have been designed under the assumption that the input text will be in reasonably neat form, e.g.,newspaper stories and other edited texts. |
tech,2-1-P82-1035,bq |
executed to yield the answer . Most large
<term>
|
text-understanding systems
|
</term>
have been designed under the assumption
|
#12946
Most largetext-understanding systems have been designed under the assumption that the input text will be in reasonably neat form, e.g., newspaper stories and other edited texts. |
other,39-4-P82-1035,bq |
<term>
elllpsis
</term>
) , and resolve
<term>
|
referents
|
</term>
(
<term>
anaphora
</term>
) . This method
|
#13094
These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolvereferents (anaphora). |
other,4-5-P82-1035,bq |
anaphora
</term>
) . This method of using
<term>
|
expectations
|
</term>
to aid the understanding of
<term>
|
#13103
This method of usingexpectations to aid the understanding of scruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages. |
other,14-4-P82-1035,bq |
out
<term>
unknown words
</term>
from
<term>
|
context
|
</term>
, constrain the possible
<term>
word-senses
|
#13069
These syntactic and semantic expectations can be used to figure out unknown words fromcontext, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |
other,10-3-P82-1035,bq |
to these problems is to make use of
<term>
|
expectations
|
</term>
, based both on knowledge of
<term>
|
#13036
Our solution to these problems is to make use ofexpectations, based both on knowledge of surface English and on world knowledge of the situation being described. |
other,26-2-P82-1035,bq |
features that differ significantly from
<term>
|
neat texts
|
</term>
, posing special problems for readers
|
#13000
However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly fromneat texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc. |