| other,16-2-P82-1035,ak | </term> e.g. , memos , rough drafts , <term> | conversation transcripts | </term> etc. , have features that differ | #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,21-3-P82-1035,ak | 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,1-4-P82-1035,ak | situation being described . These <term> | syntactic and semantic expectations | </term> can be used to figure out unknown | #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,24-4-P82-1035,ak | </term> of <term> words </term> with multiple <term> | meanings | </term> ( <term> ambiguity </term> ) , fill in | #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,21-4-P82-1035,ak | possible <term> word-senses </term> of <term> | words | </term> with multiple <term> meanings </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,26-4-P82-1035,ak | with multiple <term> meanings </term> ( <term> | ambiguity | </term> ) , fill in missing words ( <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,34-4-P82-1035,ak | </term> ) , fill in missing words ( <term> | ellipsis | </term> ) , and resolve <term> referents </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,12-1-P82-1035,ak | designed under the assumption that the <term> | input text | </term> will be in reasonably neat form , | #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,38-2-P82-1035,ak | problems for readers , such as misspelled <term> | words | </term> , missing <term> words </term> , poor | #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,10-3-P82-1035,ak | to these problems is to make use of <term> | expectations | </term> , based both on knowledge of surface | #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,6-2-P82-1035,ak | texts . However , a great deal of <term> | natural language texts | </term> e.g. , memos , rough drafts , <term> | #14269 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,26-5-P82-1035,ak | </term> , which understands scruffy <term> | texts | </term> in the <term> domain </term> of Navy | #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,41-4-P82-1035,ak | and resolve <term> referents </term> ( <term> | anaphora | </term> ) . This method of using expectations | #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). | |
| other,23-1-P82-1035,ak | be in reasonably neat form , e.g. , <term> | newspaper stories | </term> and other edited texts . However | #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. | |
| tech,2-1-P82-1035,ak | executed to yield the answer . Most large <term> | text-understanding systems | </term> have been designed under the assumption | #14235 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,29-5-P82-1035,ak | understands scruffy <term> texts </term> in the <term> | domain | </term> of Navy messages . This article deals | #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,11-5-P82-1035,ak | to aid the understanding of scruffy <term> | texts | </term> has been incorporated into a working | #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,39-4-P82-1035,ak | <term> ellipsis </term> ) , and resolve <term> | referents | </term> ( <term> anaphora </term> ) . This method | #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,19-4-P82-1035,ak | context </term> , constrain the possible <term> | word-senses | </term> of <term> words </term> with multiple | #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,44-2-P82-1035,ak | </term> , missing <term> words </term> , poor <term> | syntactic construction | </term> , missing periods , etc . Our solution | #14307 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, poorsyntactic construction, missing periods, etc. |
