Concordance

Query P82-1035 31 >
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other,27-1-P82-1035,bq	<term> 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 otheredited 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 inputtext will be in reasonably neat form, e.g., newspaper stories and other edited texts.
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,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.
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,34-4-P82-1035,bq	fill in <term> missing words </term> ( <term>	elllpsis	</term> ) , and resolve <term> referents </term>	#13089 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	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,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,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 inmissing words (elllpsis), and resolve referents (anaphora).
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.
tech,18-5-P82-1035,bq	has 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 workingcomputer program called NOMAD, which understands scruffy texts in the domain of Navy messages.
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,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,23-1-P82-1035,bq	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,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 asmisspelled words, missing words, poor syntactic construction, missing periods, etc.
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 fromcontext, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora).
other,10-3-P82-1035,bq	to 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 ofexpectations, based both on knowledge of surface English and on world knowledge of the situation being described.
other,40-2-P82-1035,bq	such 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,39-4-P82-1035,bq	<term> elllpsis </term> ) , and resolve <term>	referents	</term> ( <term> anaphora </term> ) . This method	#13094 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 resolvereferents (anaphora).


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