Difference between revisions of "Pattern Learning"
From Knowitall
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## Remove extractions with single or double letter arguments, optionally ending with a period. | ## Remove extractions with single or double letter arguments, optionally ending with a period. | ||
# Filter arguments | # Filter arguments | ||
− | ## Remove inc, ltd, vehicle, turn | + | ## Remove inc, ltd, vehicle, turn, page, site |
+ | ## Remove arguments that are 2 or fewer characters | ||
# 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). |
Revision as of 00:38, 16 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
- 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).
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