| tool,21-5-P82-1035,bq | <term> computer program </term> called <term> | NOMAD | </term> , which understands <term> scruffy | #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. |
| 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. |
| 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,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,43-2-P82-1035,bq | </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,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,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,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,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,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,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,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. |
| other,6-2-P82-1035,bq | </term> . However , a great deal of <term> | natural language texts | </term> e.g. , <term> memos </term> , rough <term> | #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,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,40-2-P82-1035,bq | such as <term> misspelled words </term> , <term> | missing words | </term> , <term> poor syntactic construction | #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,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,47-2-P82-1035,bq | poor syntactic construction </term> , <term> | missing periods | </term> , etc . Our solution to these problems | #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,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,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). |
