tech,2-1-P82-1035,bq |
Most large
<term>
text-understanding systems
</term>
have been designed under the assumption that the input
<term>
text
</term>
will be in reasonably neat form , e.g. ,
<term>
newspaper stories
</term>
and other
<term>
edited texts
</term>
.
|
#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,13-1-P82-1035,bq |
Most large
<term>
text-understanding systems
</term>
have been designed under the assumption that the input
<term>
text
</term>
will be in reasonably neat form , e.g. ,
<term>
newspaper stories
</term>
and other
<term>
edited texts
</term>
.
|
#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,23-1-P82-1035,bq |
Most large
<term>
text-understanding systems
</term>
have been designed under the assumption that the input
<term>
text
</term>
will 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,27-1-P82-1035,bq |
Most large
<term>
text-understanding systems
</term>
have been designed under the assumption that the input
<term>
text
</term>
will be in reasonably neat form , e.g. ,
<term>
newspaper stories
</term>
and other
<term>
edited texts
</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,6-2-P82-1035,bq |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</term>
,
<term>
missing words
</term>
,
<term>
poor syntactic construction
</term>
,
<term>
missing periods
</term>
, etc .
|
#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. |
other,11-2-P82-1035,bq |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</term>
,
<term>
missing words
</term>
,
<term>
poor syntactic construction
</term>
,
<term>
missing periods
</term>
, etc .
|
#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 |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</term>
,
<term>
missing words
</term>
,
<term>
poor syntactic construction
</term>
,
<term>
missing periods
</term>
, etc .
|
#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,16-2-P82-1035,bq |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</term>
,
<term>
missing words
</term>
,
<term>
poor syntactic construction
</term>
,
<term>
missing periods
</term>
, etc .
|
#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 |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</term>
,
<term>
missing words
</term>
,
<term>
poor syntactic construction
</term>
,
<term>
missing periods
</term>
, etc .
|
#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,37-2-P82-1035,bq |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</term>
,
<term>
missing words
</term>
,
<term>
poor syntactic construction
</term>
,
<term>
missing periods
</term>
, etc .
|
#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,40-2-P82-1035,bq |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</term>
,
<term>
missing words
</term>
,
<term>
poor syntactic construction
</term>
,
<term>
missing periods
</term>
, etc .
|
#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 |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</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 |
However , a great deal of
<term>
natural language texts
</term>
e.g. ,
<term>
memos
</term>
, rough
<term>
drafts
</term>
,
<term>
conversation transcripts
</term>
etc. , have features that differ significantly from
<term>
neat texts
</term>
, posing special problems for readers , such as
<term>
misspelled words
</term>
,
<term>
missing words
</term>
,
<term>
poor syntactic construction
</term>
,
<term>
missing periods
</term>
, etc .
|
#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 |
Our solution to these problems is to make use of
<term>
expectations
</term>
, based both on knowledge of
<term>
surface English
</term>
and on
<term>
world knowledge
</term>
of the situation being described .
|
#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,17-3-P82-1035,bq |
Our solution to these problems is to make use of
<term>
expectations
</term>
, based both on knowledge of
<term>
surface English
</term>
and on
<term>
world knowledge
</term>
of the situation being described .
|
#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,21-3-P82-1035,bq |
Our solution to these problems is to make use of
<term>
expectations
</term>
, based both on knowledge of
<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,1-4-P82-1035,bq |
These
<term>
syntactic and semantic expectations
</term>
can be used to figure out
<term>
unknown words
</term>
from
<term>
context
</term>
, constrain the possible
<term>
word-senses
</term>
of
<term>
words with multiple meanings
</term>
(
<term>
ambiguity
</term>
) , fill in
<term>
missing words
</term>
(
<term>
elllpsis
</term>
) , and resolve
<term>
referents
</term>
(
<term>
anaphora
</term>
) .
|
#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 |
These
<term>
syntactic and semantic expectations
</term>
can be used to figure out
<term>
unknown words
</term>
from
<term>
context
</term>
, constrain the possible
<term>
word-senses
</term>
of
<term>
words with multiple meanings
</term>
(
<term>
ambiguity
</term>
) , fill in
<term>
missing words
</term>
(
<term>
elllpsis
</term>
) , and resolve
<term>
referents
</term>
(
<term>
anaphora
</term>
) .
|
#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,14-4-P82-1035,bq |
These
<term>
syntactic and semantic expectations
</term>
can be used to figure out
<term>
unknown words
</term>
from
<term>
context
</term>
, constrain the possible
<term>
word-senses
</term>
of
<term>
words with multiple meanings
</term>
(
<term>
ambiguity
</term>
) , fill in
<term>
missing words
</term>
(
<term>
elllpsis
</term>
) , and resolve
<term>
referents
</term>
(
<term>
anaphora
</term>
) .
|
#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,19-4-P82-1035,bq |
These
<term>
syntactic and semantic expectations
</term>
can be used to figure out
<term>
unknown words
</term>
from
<term>
context
</term>
, constrain the possible
<term>
word-senses
</term>
of
<term>
words with multiple meanings
</term>
(
<term>
ambiguity
</term>
) , fill in
<term>
missing words
</term>
(
<term>
elllpsis
</term>
) , and resolve
<term>
referents
</term>
(
<term>
anaphora
</term>
) .
|
#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). |