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,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). |
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 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,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 of scruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages. |
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,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,4-5-P82-1035,bq |
anaphora
</term>
) . This method of using
<term>
|
expectations
|
</term>
to aid the understanding of
<term>
|
#13103
This method of using expectations 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,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 understands scruffy texts in the domain of Navy messages. |
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 called NOMAD , which understands scruffy texts in the domain of Navy messages. |
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,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. |
tech,18-5-P82-1035,bq |
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 working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages. |
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,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,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). |
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,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,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 in missing words (elllpsis), and resolve referents (anaphora). |
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. |