Integrating Local Context and Global Cohesivenessfor Open Information Extraction
Table 3. Performance comparison with state-of-the-art entity phrase extraction algorithms for the weakly-supervised entity phrase extraction task.
['[BOLD] Methods', '[BOLD] NYT ( riedel2013relation,  ) F1', '[BOLD] NYT ( riedel2013relation,  ) Prec', '[BOLD] NYT ( riedel2013relation,  ) Rec', '[BOLD] Wiki-KBP ( ling2012fine,  ) F1', '[BOLD] Wiki-KBP ( ling2012fine,  ) Prec', '[BOLD] Wiki-KBP ( ling2012fine,  ) Rec']
1. Performance on Entity Phrase Extraction. The training data is generated through distant supervision described above without type information. Regarding open domain extractions, we train baseline models using the same distant supervision as ReMine, to push them towards a fair comparison. In the Wiki-KBP dataset, ReMine evidently outperforms all the other baselines. In the NYT dataset, ReMine has a rather high recall and is on par with the two neural network models on F1-score.