Revisiting Unsupervised Relation Extraction
Table 1: Average results (%) across three runs of different models (except the EType) on NYT-FB and TACRED. c indicates the number of clusters in each method. ⋄ indicates our implementation of the corresponding model. We note that all methods were trained on NYT-FB and evaluated on the test set of both NYT-FB and TACRED.
['[BOLD] Model NYT-FB', '[BOLD] Model NYT-FB', '[BOLD] B3 NYT-FB', '[BOLD] V NYT-FB', '[BOLD] ARI NYT-FB']
Our contributions are as follows: (i) We perform experiments on both automatically/manually-labelled datasets, namely NYT-FB and TACRED, respectively. We show that two methods using only entity types can outperform the state-of-the-art models including both feature-engineering and deep learning approaches. The surprising results raise questions about the current state of unsupervised relation extraction. (ii) For model design, we show that link predictor provides a good signal to train a URE model (Fig 1).