Concordance

Query P82-1035 31 >
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other,47-2-P82-1035,bq	However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .	#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.
other,16-2-P82-1035,bq	However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .	#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,23-1-P82-1035,bq	Most large <term> text-understanding systems </term> have been designed under the assumption that the input <term> text </term> will 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,11-2-P82-1035,bq	However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .	#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,43-2-P82-1035,bq	However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </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,40-2-P82-1035,bq	However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .	#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,26-4-P82-1035,bq	These <term> syntactic and semantic expectations </term> can be used to figure out <term> unknown words </term> from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words with multiple meanings </term> ( <term> ambiguity </term> ) , fill in <term> missing words </term> ( <term> elllpsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </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,41-4-P82-1035,bq	These <term> syntactic and semantic expectations </term> can be used to figure out <term> unknown words </term> from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words with multiple meanings </term> ( <term> ambiguity </term> ) , fill in <term> missing words </term> ( <term> elllpsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#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,34-4-P82-1035,bq	These <term> syntactic and semantic expectations </term> can be used to figure out <term> unknown words </term> from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words with multiple meanings </term> ( <term> ambiguity </term> ) , fill in <term> missing words </term> ( <term> elllpsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </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).
other,37-2-P82-1035,bq	However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .	#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.
tool,21-5-P82-1035,bq	This method of using <term> expectations </term> to aid the understanding of <term> scruffy texts </term> has been incorporated into a working <term> computer program </term> called <term> NOMAD </term> , which understands <term> scruffy texts </term> in the domain of Navy messages .	#13120 This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program calledNOMAD, which understands scruffy texts in the domain of Navy messages.
other,26-2-P82-1035,bq	However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .	#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,14-4-P82-1035,bq	These <term> syntactic and semantic expectations </term> can be used to figure out <term> unknown words </term> from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words with multiple meanings </term> ( <term> ambiguity </term> ) , fill in <term> missing words </term> ( <term> elllpsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#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,31-4-P82-1035,bq	These <term> syntactic and semantic expectations </term> can be used to figure out <term> unknown words </term> from <term> context </term> , constrain the possible <term> word-senses </term> of <term> words with multiple meanings </term> ( <term> ambiguity </term> ) , fill in <term> missing words </term> ( <term> elllpsis </term> ) , and resolve <term> referents </term> ( <term> anaphora </term> ) .	#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,13-1-P82-1035,bq	Most large <term> text-understanding systems </term> have been designed under the assumption that the input <term> text </term> will be in reasonably neat form , e.g. , <term> newspaper stories </term> and other <term> edited texts </term> .	#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.
tech,2-1-P82-1035,bq	Most large <term> text-understanding systems </term> have been designed under the assumption that the input <term> text </term> will be in reasonably neat form , e.g. , <term> newspaper stories </term> and other <term> edited texts </term> .	#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,6-2-P82-1035,bq	However , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term> drafts </term> , <term> conversation transcripts </term> etc. , have features that differ significantly from <term> neat texts </term> , posing special problems for readers , such as <term> misspelled words </term> , <term> missing words </term> , <term> poor syntactic construction </term> , <term> missing periods </term> , etc .	#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,17-3-P82-1035,bq	Our solution to these problems is to make use of <term> expectations </term> , based both on knowledge of <term> surface English </term> and on <term> world knowledge </term> of the situation being described .	#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,10-5-P82-1035,bq	This method of using <term> expectations </term> to aid the understanding of <term> scruffy texts </term> has been incorporated into a working <term> computer program </term> called <term> NOMAD </term> , which understands <term> scruffy texts </term> in the domain of Navy messages .	#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,10-3-P82-1035,bq	Our solution to these problems is to make use of <term> expectations </term> , based both on knowledge of <term> surface English </term> and on <term> world knowledge </term> of the situation being described .	#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.


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