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). |
tech,18-5-P82-1035,bq |
has been incorporated into a working
<term>
|
computer program
|
</term>
called
<term>
NOMAD
</term>
, which understands
|
#13117
This method of using expectations to aid the understanding of scruffy texts has been incorporated into a workingcomputer program called NOMAD, which understands scruffy texts in the domain of Navy messages. |
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,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. |
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,26-4-P82-1035,bq |
words with multiple meanings
</term>
(
<term>
|
ambiguity
|
</term>
) , fill in
<term>
missing words
</term>
|
#13081
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,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. |
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,47-2-P82-1035,bq |
poor syntactic construction
</term>
,
<term>
|
missing periods
|
</term>
, etc . Our solution to these problems
|
#13021
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,17-3-P82-1035,bq |
</term>
, based both on knowledge of
<term>
|
surface English
|
</term>
and on
<term>
world knowledge
</term>
|
#13043
Our solution to these problems is to make use of expectations, based both on knowledge ofsurface English and on world knowledge of the situation being described. |
other,11-2-P82-1035,bq |
natural language texts
</term>
e.g. ,
<term>
|
memos
|
</term>
, rough
<term>
drafts
</term>
,
<term>
|
#12985
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,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,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,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,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,40-2-P82-1035,bq |
such as
<term>
misspelled words
</term>
,
<term>
|
missing words
|
</term>
,
<term>
poor syntactic construction
|
#13014
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,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,31-4-P82-1035,bq |
(
<term>
ambiguity
</term>
) , fill in
<term>
|
missing words
|
</term>
(
<term>
elllpsis
</term>
) , and resolve
|
#13086
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 inmissing 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. |
other,16-2-P82-1035,bq |
</term>
, rough
<term>
drafts
</term>
,
<term>
|
conversation transcripts
|
</term>
etc. , have features that differ
|
#12990
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. |