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 UNRELATED26808528913529559092.790.791.7 org:city_of_ headquarters58359040751464.577.770.5 org:country_of_ headquarters28514638372561.576.568.2 org:founded38968199666247.558.552.4 org:parents11582292252550.545.948.1 org:top_members/empl oyees12823067359641.835.738.5 per:city_of_birth17993920325245.955.350.2 per:country_of_birth19844122320448.161.954.2 per:date_of_birth39385427436272.690.380.5 per:member_of17713018288758.761.360 per:title1714336430545156.153.4 Total3716968822623675459.656.7 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)