| tech,2-1-P82-1035,bq | executed to yield the answer . Most large <term> | text-understanding systems | </term> have been designed under the assumption | #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,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,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. |
| 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,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,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,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,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,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,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,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,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,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,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,21-3-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,1-4-P82-1035,bq | situation being described . These <term> | syntactic and semantic expectations | </term> can be used to figure out <term> unknown | #13056 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 (elllpsis), and resolve referents (anaphora). |
| 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,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,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,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). |
