| 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,41-4-P82-1035,bq | and resolve <term> referents </term> ( <term> | anaphora | </term> ) . This method of using <term> expectations | #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). |
| 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,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). |
| 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,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,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,34-4-P82-1035,bq | fill in <term> missing words </term> ( <term> | elllpsis | </term> ) , and resolve <term> referents </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,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,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,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,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,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,37-2-P82-1035,bq | special problems for readers , such as <term> | misspelled words | </term> , <term> missing words </term> , <term> | #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. |
| 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,26-2-P82-1035,bq | features that differ significantly from <term> | neat texts | </term> , posing special problems for readers | #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,23-1-P82-1035,bq | 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. |
| 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. |
| 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. |
