@article {22317,
	title = {Role-based Association of verbs, actions, and sentiments with entities in political discourse.},
	journal = {Communication Methods and Measures},
	volume = {13},
	number = {2},
	year = {2019},
	month = {2019},
	pages = {69-82},
	abstract = {A crucial challenge in measuring how text represents an entity is the need to associate each representative expression with a relevant entity to generate meaningful results. Common solutions to this problem are usually based on proximity methods that require a large corpus to reach reasonable levels of accuracy. We show how such methods for the association between an entity and a representation yield a high percentage of false positives at the expression level and low validity at the document level. We introduce a solution that combines syntactic parsing, semantic role labeling logic, and a machine learning approach{\textemdash}the role-based association method. To test our method, we compared it with prevalent methods of association on the news coverage of two entities of interest{\textemdash}the State of Israel and the Palestinian Authority. We found that the role-based association method is more accurate at the expression and the document levels.},
	url = {https://www.tandfonline.com/doi/full/10.1080/19312458.2018.1536973},
	author = {Y.air Fogel-Dror and Shenhav, Shaul and Sheafer, Tamir and Van Atteveldt, Wouter}
}
