Concordance

Query P82-1035 24 >
Multilevel Sort 24 (768.4 per million)

other,1-4-P82-1035,ak	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14345 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 (ellipsis), and resolve referents (anaphora).
other,10-3-P82-1035,ak	Our solution to these problems is to make use of <term> expectations </term> , based both on knowledge of surface English and on <term> world knowledge </term> of the situation being described .	#14325 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,11-5-P82-1035,ak	This method of using expectations to aid the understanding of scruffy <term> texts </term> has been incorporated into a working computer program called <term> NOMAD </term> , which understands scruffy <term> texts </term> in the <term> domain </term> of Navy messages .	#14399 This 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,12-1-P82-1035,ak	Most large <term> text-understanding systems </term> have been designed under the assumption that the <term> input text </term> will be in reasonably neat form , e.g. , <term> newspaper stories </term> and other edited texts .	#14245 Most 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,14-4-P82-1035,ak	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14358 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 (ellipsis), and resolve referents (anaphora).
other,16-2-P82-1035,ak	However , a great deal of <term> natural language texts </term> e.g. , memos , rough drafts , <term> conversation transcripts </term> etc. , have features that differ significantly from neat <term> texts </term> , posing special problems for readers , such as misspelled <term> words </term> , missing <term> words </term> , poor <term> syntactic construction </term> , missing periods , etc .	#14279 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,19-4-P82-1035,ak	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14363 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 (ellipsis), and resolve referents (anaphora).
other,21-3-P82-1035,ak	Our solution to these problems is to make use of <term> expectations </term> , based both on knowledge of surface English and on <term> world knowledge </term> of the situation being described .	#14336 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,ak	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14365 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 (ellipsis), and resolve referents (anaphora).
other,23-1-P82-1035,ak	Most large <term> text-understanding systems </term> have been designed under the assumption that the <term> input text </term> will be in reasonably neat form , e.g. , <term> newspaper stories </term> and other edited texts .	#14256 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,24-4-P82-1035,ak	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14368 These 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,26-4-P82-1035,ak	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14370 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 (ellipsis), and resolve referents (anaphora).
other,26-5-P82-1035,ak	This method of using expectations to aid the understanding of scruffy <term> texts </term> has been incorporated into a working computer program called <term> NOMAD </term> , which understands scruffy <term> texts </term> in the <term> domain </term> of Navy messages .	#14414 This 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.
other,27-2-P82-1035,ak	However , a great deal of <term> natural language texts </term> e.g. , memos , rough drafts , <term> conversation transcripts </term> etc. , have features that differ significantly from neat <term> texts </term> , posing special problems for readers , such as misspelled <term> words </term> , missing <term> words </term> , poor <term> syntactic construction </term> , missing periods , etc .	#14290 However, 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,29-5-P82-1035,ak	This method of using expectations to aid the understanding of scruffy <term> texts </term> has been incorporated into a working computer program called <term> NOMAD </term> , which understands scruffy <term> texts </term> in the <term> domain </term> of Navy messages .	#14417 This 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	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14378 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 (ellipsis), and resolve referents (anaphora).
other,38-2-P82-1035,ak	However , a great deal of <term> natural language texts </term> e.g. , memos , rough drafts , <term> conversation transcripts </term> etc. , have features that differ significantly from neat <term> texts </term> , posing special problems for readers , such as misspelled <term> words </term> , missing <term> words </term> , poor <term> syntactic construction </term> , missing periods , etc .	#14301 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 misspelledwords, missing words, poor syntactic construction, missing periods, etc.
other,39-4-P82-1035,ak	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14383 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 (ellipsis), and resolvereferents (anaphora).
other,41-2-P82-1035,ak	However , a great deal of <term> natural language texts </term> e.g. , memos , rough drafts , <term> conversation transcripts </term> etc. , have features that differ significantly from neat <term> texts </term> , posing special problems for readers , such as misspelled <term> words </term> , missing <term> words </term> , poor <term> syntactic construction </term> , missing periods , etc .	#14304 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, missingwords, poor syntactic construction, missing periods, etc.
other,41-4-P82-1035,ak	These <term> syntactic and semantic expectations </term> can be used to figure out unknown words from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words </term> with multiple <term> meanings </term> ( <term> ambiguity </term> ) , fill in missing words ( <term> ellipsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#14385 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 (ellipsis), and resolve referents (anaphora).


	in Help