Abstract

While most natural language understanding systems rely on a pipeline-based architecture, certain human text interpretation methods are based on a cyclic process between the whole text and its parts: the hermeneutic circle. In the task of automatically identifying characters and their narrative roles, we propose a feedback-loop-based approach where the output of later modules of the pipeline is fed back to earlier ones. We analyze this approach using a corpus of 21 Russian folktales. Initial results show that feeding back high-level narrative information improves the performance of some NLP tasks.

Abstract

Narrative Hermeneutic Circle: Improving Character Role Identification from Natural Language Text via Feedback Loops / 2517 Josep Valls-Vargas, Jichen Zhu, Santiago Ontanon PDF