Difference between revisions of "Pattern Learning"
From Knowitall
(→Determining target extractions) |
(→Determining target extractions) |
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# Choose the most frequent relations from this set | # Choose the most frequent relations from this set | ||
− | == Determining target extractions == | + | == Determining target extractions (seeds) == |
# Start with the clean, chunked dataset of ReVerb extractions from ClueWeb. | # Start with the clean, chunked dataset of ReVerb extractions from ClueWeb. | ||
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# Measure the occurrence of the arguments. | # Measure the occurrence of the arguments. | ||
# Keep extractions from the target relations that have arguments that occur commonly (20 times). | # Keep extractions from the target relations that have arguments that occur commonly (20 times). | ||
+ | # Remove target relations which have fewer than 15 seeds | ||
== Lemma grep == | == Lemma grep == |
Revision as of 19:50, 17 November 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 (seeds)
- 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.
- Filter arguments
- Remove inc, ltd, vehicle, turn, page, site
- Remove arguments that are 2 or fewer characters
- Measure the occurrence of the arguments.
- Keep extractions from the target relations that have arguments that occur commonly (20 times).
- Remove target relations which have fewer than 15 seeds
Lemma grep
- Search corpus for all sentences that contain the lemmas in a target extraction.
- Remove duplicate sentences (sentence*extraction pairs must be unique).
- For each sentence*extraction pair, search for a pattern that connects the lemmas.
- Pattern must start with the arg1
Reducing the patterned results
- Remove patterns that occur less than 5 times.
- 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