Concordance

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

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,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,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,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,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,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).
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,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,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,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.
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.


	in Help