Year: 2003
Type: conference-paper
Abstract: Many applications of natural language processing technologies involve analyzing texts that concern the psychological states and processes of people, including their beliefs, goals, predictions, explanations, and plans. In this paper, we describe our efforts to create a robust, large-scale lexical-semantic resource for the recognition and classification of expressions of commonsense psychology in English Text. We achieve high levels of precision and recall by hand-authoring sets of local grammars for commonsense psychology concepts, and show that this approach can achieve classification performance greater than that obtained by using machine learning techniques. We demonstrate the utility of this resource for large-scale corpus analysis by identifying references to adversarial and competitive goals in political speeches throughout U.S. history.
Authors: Andrew Gordon, Abe Kazemzadeh, Anish Nair, Milena Petrova
Institution: University of Southern California
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2101110304