Concordance

Query P82-1035 31 >
Multilevel Sort 31 >
Multilevel Sort 31 (1,445.3 per million)

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,10-3-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,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,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.
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,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,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, 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,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,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,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 ofsurface English and on world 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,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,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 ofwords 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,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,26-2-P82-1035,bq	features that differ significantly from <term>	neat texts	</term> , posing special problems for readers	#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,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,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,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,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).


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