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 III: Accuracy (%) of our Full Model, of a system based on Word-Level (WL) Encoding, and of a system not-provided with information on the recently disambiguated instances.
['[EMPTY]', '[BOLD] Entities All', '[BOLD] Entities Last', '[BOLD] Relations All', '[BOLD] Relations Last']
A first comparison regards the benefits of using a character-level encoding for mentions with respect to classical word-level embedding approaches. To this end, we built a vocabulary of words, including all the correct-spelled words of our dataset (and an out-of-vocabulary token), and we limited the character-based encoder to such words, thus simulating a word-level encoder. Our model is able to encode in a meaningful way those words that contain typos, and to exploit them in context encoding, while the word-level encoder faces several out-of-vocabulary words, that also creates ambiguity while comparing contexts. Another variant of our model discards the temporal hypothesis \boldmathp(t) when disambiguating entities, and considers the hypotheses \boldmathp(z) and \boldmathp(e) only. This is not only due to its positive effects in co-reference resolution, but also when disambiguating mentions to the main entity of the story. Finally, thanks to \boldmathp(t), the system learns to develop the tendency of associating new mentions to already existing instances, instead of creating new ones, that is an inherent feature of each story (in the worst case it creates only 183 entity instances).