| 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,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,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,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,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,17-3-P82-1035,bq | </term> , based both on knowledge of <term> | surface English | </term> and on <term> world knowledge </term> | #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,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,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,21-3-P82-1035,bq | <term> surface English </term> and on <term> | world knowledge | </term> of the situation being described | #13047 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,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). |
| 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. |
