Difference between revisions of "Pattern Learning"
From Knowitall
(→Determining target extractions) |
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== Determining target extractions == | == Determining target extractions == | ||
| − | # | + | # Start with the clean, chunked dataset of ReVerb extractions from ClueWeb. |
| − | # | + | # Apply Jonathan Berant's relation string normalization. |
| − | # | + | # Filter relations so each relation's normalized relation string matches a target relation and the arguments only contain DT, NNP, and NNPS. |
| − | # | + | # Filter extractions |
| − | # | + | ## Remove extraction strings that occur less than three times. |
| − | # | + | ## Remove extractions with single or double letter arguments, optionally ending with a period. |
| + | # Measure the occurrence of the arguments. | ||
| + | # Keep extractions from the target relations that have arguments that occur commonly (20 times). | ||
== Reducing the lemma grep results == | == Reducing the lemma grep results == | ||
Revision as of 19:27, 25 October 2011
Contents
Building the boostrapping data
Determining target relations
- Restrict high quality set of ClueWeb extractions to have proper noun arguments
- Choose the most frequent relations from this set
Determining target extractions
- Start with the clean, chunked dataset of ReVerb extractions from ClueWeb.
- Apply Jonathan Berant's relation string normalization.
- Filter relations so each relation's normalized relation string matches a target relation and the arguments only contain DT, NNP, and NNPS.
- Filter extractions
- Remove extraction strings that occur less than three times.
- Remove extractions with single or double letter arguments, optionally ending with a period.
- Measure the occurrence of the arguments.
- Keep extractions from the target relations that have arguments that occur commonly (20 times).
Reducing the lemma grep results
- Remove patterns that occur less than 5 times.
- Remove duplicate sentences.
- Remove extractions that have an (extraction, pattern) pairs that occurs anomalously frequently.
- There was a single one: (hotel reservation, be make, online) ocurred 32k times, the next one ocurred 8k times
