Concordance

Query P82-1035 31 >
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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,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 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,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,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 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,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 ofscruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages.
tech,2-1-P82-1035,bq	executed to yield the answer . Most large <term>	text-understanding systems	</term> have been designed under the assumption	#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,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 understandsscruffy texts in the domain of Navy messages.
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,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,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,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.


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