CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
Table 7: Performance comparison on end-to-end relation extraction (at the highest F1 point) on the three datasets.
['[BOLD] Method', '[BOLD] NYT\xa0  [BOLD] Prec', '[BOLD] NYT\xa0  [BOLD] Rec', '[BOLD] NYT\xa0  [BOLD] F1', '[BOLD] NYT\xa0  [BOLD] Time', '[BOLD] Wiki-KBP\xa0  [BOLD] Prec', '[BOLD] Wiki-KBP\xa0  [BOLD] Rec', '[BOLD] Wiki-KBP\xa0  [BOLD] F1', '[BOLD] Wiki-KBP\xa0  [BOLD] Time', '[BOLD] BioInfer\xa0  [BOLD] Prec', '[BOLD] BioInfer\xa0  [BOLD] Rec', '[BOLD] BioInfer\xa0  [BOLD] F1', '[BOLD] BioInfer\xa0  [BOLD] Time']
3. Performance on Relation Extraction. To test the domain independence of CoType framework, we conduct evaluations on the end-to-end relation extraction. As only MultiR and DS-Joint are able to detection entity and relation mentions in their own framework, we apply our detection results to other compared methods. In particular, results at each method’s highest F1 score point are reported, after tuning the threshold for each method for determining whether a test mention is None or some target type. Overall, CoType outperforms all other methods on F1 score on all three datasets. We observe that DS-Joint and MultiR suffer from low recall, since their entity detection modules do not work well on DL (where many tokens have false negative tags). This demonstrates the effectiveness of the proposed domain-agnostic text segmentation algorithm (see Sec. We found that the incremental diagram of learning embedding (i.e., CoType-TwoStep) brings only marginal improvement. In Fig. BioInfer datasets further show that CoType can still achieve descent precision with good recall preserved. 4. Scalability. CoType demonstrates a linear runtime trend (which validates our time complexity in Sec.