Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
TABLE I: Accuracy (%) for different amounts of supervision in the Wikifacts Dataset.
['[EMPTY]', 'Model', '10%', '25%', '50%', '90%']
Our system outperforms the competitors in all our experiments. Differently from Deep-RNN, our model exploits its capability of building local models of the instances, while Deep-RNN is not able to capture this locality, either not-learning enough or overfitting supervisions. Moreover, Deep-RNN has difficulties in storing information on the whole story, as shown in the LAST case.