#14370These 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 (ellipsis), and resolve referents (anaphora).
other,41-4-P82-1035,ak
and resolve
<term>
referents
</term>
(
<term>
anaphora
</term>
) . This method of using expectations
#14385These 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 (ellipsis), and resolve referents (anaphora).
other,14-4-P82-1035,ak
used to figure out unknown words from
<term>
context
</term>
, constrain the possible
<term>
word-senses
#14358These 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 (ellipsis), and resolve referents (anaphora).
other,16-2-P82-1035,ak
</term>
e.g. , memos , rough drafts ,
<term>
conversation transcripts
</term>
etc. , have features that differ
#14279However, 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,29-5-P82-1035,ak
understands scruffy
<term>
texts
</term>
in the
<term>
domain
</term>
of Navy messages . This article deals
#14417This 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 thedomain of Navy messages.
other,34-4-P82-1035,ak
</term>
) , fill in missing words (
<term>
ellipsis
</term>
) , and resolve
<term>
referents
</term>
#14378These 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 (ellipsis), and resolve referents (anaphora).
other,10-3-P82-1035,ak
to these problems is to make use of
<term>
expectations
</term>
, based both on knowledge of surface
#14325Our 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,12-1-P82-1035,ak
designed under the assumption that the
<term>
input text
</term>
will be in reasonably neat form ,
#14245Most large text-understanding systems have been designed under the assumption that theinput text will be in reasonably neat form, e.g., newspaper stories and other edited texts.
other,24-4-P82-1035,ak
</term>
of
<term>
words
</term>
with multiple
<term>
meanings
</term>
(
<term>
ambiguity
</term>
) , fill in
#14368These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiplemeanings (ambiguity), fill in missing words (ellipsis), and resolve referents (anaphora).
other,6-2-P82-1035,ak
texts . However , a great deal of
<term>
natural language texts
</term>
e.g. , memos , rough drafts ,
<term>
#14269However, 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,23-1-P82-1035,ak
be in reasonably neat form , e.g. ,
<term>
newspaper stories
</term>
and other edited texts . However
#14256Most 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.
tool,21-5-P82-1035,ak
a working computer program called
<term>
NOMAD
</term>
, which understands scruffy
<term>
#14409This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program calledNOMAD, which understands scruffy texts in the domain of Navy messages.
other,39-4-P82-1035,ak
<term>
ellipsis
</term>
) , and resolve
<term>
referents
</term>
(
<term>
anaphora
</term>
) . This method
#14383These 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 (ellipsis), and resolvereferents (anaphora).
other,1-4-P82-1035,ak
situation being described . These
<term>
syntactic and semantic expectations
</term>
can be used to figure out unknown
#14345Thesesyntactic 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 (ellipsis), and resolve referents (anaphora).
other,44-2-P82-1035,ak
</term>
, missing
<term>
words
</term>
, poor
<term>
syntactic construction
</term>
, missing periods , etc . Our solution
#14307However, 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, poorsyntactic construction, missing periods, etc.
other,27-2-P82-1035,ak
that differ significantly from neat
<term>
texts
</term>
, posing special problems for readers
#14290However, a great deal of natural language texts e.g., memos, rough drafts, conversation transcripts etc., have features that differ significantly from neattexts, posing special problems for readers, such as misspelled words, missing words, poor syntactic construction, missing periods, etc.
other,11-5-P82-1035,ak
to aid the understanding of scruffy
<term>
texts
</term>
has been incorporated into a working
#14399This method of using expectations to aid the understanding of scruffytexts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages.
other,26-5-P82-1035,ak
</term>
, which understands scruffy
<term>
texts
</term>
in the
<term>
domain
</term>
of Navy
#14414This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffytexts in the domain of Navy messages.
tech,2-1-P82-1035,ak
executed to yield the answer . Most large
<term>
text-understanding systems
</term>
have been designed under the assumption
#14235Most 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,38-2-P82-1035,ak
problems for readers , such as misspelled
<term>
words
</term>
, missing
<term>
words
</term>
, poor
#14301However, 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 misspelledwords, missing words, poor syntactic construction, missing periods, etc.