Extracting higher-order logic formulas from English sentences
Alexandre Rademaker, Guilherme Augusto Ferreira Lima, et al.
ICNLSP 2023
With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is. Prior works relied on Wikipedia’s metadata and on content analysis of the articles pertaining to a concept in question. Here we show that the immediate textual context of a concept is strongly indicative of this property, and, using simple and language-independent machine-learning tools, we leverage this observation to achieve state-of-the-art results in controversiality prediction. In addition, we analyze and make available a new dataset of concepts labeled for controversiality. It is significantly larger than existing datasets, and grades concepts on a 0-10 scale, rather than treating controversiality as a binary label.
Alexandre Rademaker, Guilherme Augusto Ferreira Lima, et al.
ICNLSP 2023
Oktie Hassanzadeh, Parul Awasthy, et al.
ISWC 2022
Yangkai Du, Tengfei Ma, et al.
EMNLP 2021
Samuel Ackerman, Ella Rabinovich, et al.
EMNLP 2024