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Distant Supervision for Knowledge Base Population Mihai Surdeanu, David McClosky, John Bauer, Julie Tibshirani, Angel Chang, Valentin Spitkovsky, Christopher.

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Presentation on theme: "Distant Supervision for Knowledge Base Population Mihai Surdeanu, David McClosky, John Bauer, Julie Tibshirani, Angel Chang, Valentin Spitkovsky, Christopher."— Presentation transcript:

1 Distant Supervision for Knowledge Base Population Mihai Surdeanu, David McClosky, John Bauer, Julie Tibshirani, Angel Chang, Valentin Spitkovsky, Christopher Manning

2 Definition and Approach We took part in TAC KBP 2010 this year (both tasks) Slot filling task: learning a pre-defined set of relations and attributes for target entities based on documents in a collection – “Warren Buffett began studying at the Warton School of Finance at the University of Pennsylvania, but transferred to the University of Nebraska where he graduated.” (per:schools_attended, Warren Buffett, University of Pennsylvania) (per:schools_attended, Warren Buffett, University of Nebraska Distant supervision approach: generate training data automatically from Wikipedia infoboxes

3 Infobox KB Map infobox fields to KBP slots (one to many mapping) IR: find relevant sentences Query: entity name + slot value Extract +/- slot candidates Train multiclass classifier Map KBP slots to fine-grained NE labels KBP query: entity name IR: find relevant sentences Query: entity name + trigger words Extract slot candidates Classify candidates Inference (greedy, local) TrainingEvaluation Extracted slots

4 Results LabelCorrectPredictActualPRF1 UNRELATED org:city_of_ headquarters org:country_of_ headquarters org:founded org:parents org:top_members/empl oyees per:city_of_birth per:country_of_birth per:date_of_birth per:member_of per:title Total Training on 2/3 of infoboxes, evaluating on 1/3 Evaluating only on sentences that contain at least a valid slot Top 10 most common slots Total for all slots

5 Challenges Improve quality of data generated through distant supervision Improve IR recall – Use relation-specific trigger words (or n-grams or dependency paths etc.) to boost sentences likely to contain answers to the top – How to acquire these automatically? Better classifiers for noisy text (e.g., web snippets)


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