Narrative Hermeneutic Circle: Improving Character Role Identification from Natural Language Text via Feedback Loops
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Artificial Intelligence and the Arts. Pages 2517-2522.
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.
@inproceedings{ijcai2015p357,
title = {Narrative Hermeneutic Circle: Improving Character Role Identification from Natural Language Text via Feedback Loops},
author = {Valls-Vargas, Josep and Zhu, Jichen and Ontañón, Santiago},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {2517--2522},
year = {2015},
month = {7},
note = {Special Track on Artificial Intelligence and the Arts},
url = {https://ijcai.org/proceedings/2015/357},
}
TY - CPAPER TI - Narrative Hermeneutic Circle: Improving Character Role Identification from Natural Language Text via Feedback Loops AU - Valls-Vargas, Josep AU - Zhu, Jichen AU - Ontañón, Santiago T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 2517 EP - 2522 UR - https://ijcai.org/proceedings/2015/357 ER -Copied
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