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 II: Accuracy (%) in the case of pronouns.
['[BOLD] Supervision', '10%', '25%', '50%', '90%']
In the Simple Story Dataset, we also investigate the behaviour of the model at different time instants, using 50% of the supervisions (entities). Basically, we paused the system at a certain point of the stream, measured the accuracy, and activated the system again. This process is repeated until the end of the stream is reached. Our model reaches better results than RB after having read roughly the 20−25% of the input stream. We analyzed this result, and it turned out that this is mostly due to the fact that our system takes some time to learn how to handle the temporal information (\boldmathp(t)) needed to resolve co-references.