Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis.
Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining / Passon M.; Lippi M.; Serra G.; Tasso C.. - ELETTRONICO. - (2018), pp. 35-39. (Intervento presentato al convegno 5th Workshop on Argument Mining, co-located with EMNLP 2018 tenutosi a bel nel 2018).
Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
Lippi M.;
2018
Abstract
Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.