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,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,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,13-1-P82-1035,bq |
under the assumption that the input
<term>
|
text
|
</term>
will be in reasonably neat form ,
|
#12957
Most large text-understanding systems have been designed under the assumption that the inputtext will be in reasonably neat form, e.g., newspaper stories and other edited texts. |
other,14-2-P82-1035,bq |
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
|
drafts
|
</term>
,
<term>
conversation transcripts
</term>
|
#12988
However, a great deal of natural language texts e.g., memos, roughdrafts, 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,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,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. |
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,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,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,21-4-P82-1035,bq |
possible
<term>
word-senses
</term>
of
<term>
|
words with multiple meanings
|
</term>
(
<term>
ambiguity
</term>
) , fill in
|
#13076
These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses ofwords 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. |
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. |
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,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,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,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,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). |