tech,2-1-P82-1035,bq |
to yield the answer . Most large
<term>
|
text-understanding
systems
|
</term>
have been designed under the assumption
|
#12946
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,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 input text will be in reasonably neat form, e.g., newspaper stories and other edited texts. |
other,23-1-P82-1035,bq |
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,27-1-P82-1035,bq |
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 other edited texts. |
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 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,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,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, 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,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,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 from neat texts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc. |
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 as misspelled words, missing words, poor syntactic construction, missing periods, etc. |
other,40-2-P82-1035,bq |
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,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,10-3-P82-1035,bq |
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 of expectations , based both on knowledge of surface English and on world knowledge of the situation being described. |
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 of surface English and on world knowledge of the situation being described. |
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 on world knowledge of the situation being described. |
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
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,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 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,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 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 possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |