Wednesday, February 9, 2011
Pre-meeting Post from Weisi Duan
I have read the paper "Distant supervision for relation extraction without labeled data" by Mike Mintz et al.. The paper serves as the basis of the focus paper. The main idea is to use distant supervision to bootstrap the training instances, and then use a multi-class logistic regression classifier to predict the relation. The features are key part. The authors utilized both lexical features and the syntactic features, and compared the performance of using both against each individually. The evaluation is done in the similar way as the focus paper, since the focus paper is based on this paper. The authors sampled 1% of the positive cases to be used as negative cases, while this is intuitively plausible, I wonder what is the theoretical foundation for it. I don't see exactly that this would fall as bias variance trade off. The author could evaluate on recall to make the results stronger, which I feel might be presented to show how much the conjunctive features (which tend to induce high precision) affect the recall.