We made a mistake that we tested the performance of the previous approaches (Mintz '09, MultiR'11, with the dataset of NYT'10 which contains much more instances labeled by NG, but plotted the precision-recall curve of our DRMC-b and DRMC-1 approaches without considering the NG-labeled testing instances. To make up the erroneous experiments, we filter out the instances with NG labels in the NYT'10 dataset and test the instances labeled by at least one positive relation in this paper. Table 1 shows the statistics of the training and testing sets. We re-run the experiments of method comparison among Mintz'09, MultiR'11, MIML'12, MIML-at-least-one'12, DRMC-b and DRMC-1, and show the results in the replaced figures ( Figure 4 and Figure 6 ). Hopefully, our algorithms still significantly outperform the state-of-art methods. Therefore, the claims in our ACL'14 paper still hold. This errata paper can be cited as: For who are interested in our studies, please do refer to this updated version. Thanks so much to Danqi Chen in Stanford University who pointed out the mistakes. We sincerely apologize for the carelessness. In the future, we look forward to exploring better approaches on completing large-scale matrices that include more training instances with NG labels for distantly supervised relation extraction task.  The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (entity pairs) in a sparse matrix that concatenates training and testing textual features with training labels. Our algorithmic framework is based on the assumption that the rank of item-by-feature and item-by-label joint matrix is low. We apply two optimization models to recover the underlying low-rank matrix leveraging the sparsity of featurelabel matrix. The matrix completion problem is then solved by the fixed point continuation (FPC) algorithm, which can find the global optimum. Experiments on two widely used datasets with different dimensions of textual features demonstrate that our low-rank matrix completion approach significantly outperforms the baseline and the state-of-the-art methods.
In REF , the distantly supervised relation extraction was solved as a matrix completion problem.
Errata: Distant Supervision for Relation Extraction with Matrix Completion
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