ML Anthology

Narrative Hermeneutic Circle: Improving Character Role Identification from Natural Language Text via Feedback Loops

Josep Valls-Vargas, Jichen Zhu, Santiago Ontañón
IJCAI 2015 pp. 2517-2523
/ijcai/2015/vallsvargas2015ijcai-narrative/

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.

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Cite

Text

Valls-Vargas et al. "Narrative Hermeneutic Circle: Improving Character Role Identification from Natural Language Text via Feedback Loops." International Joint Conference on Artificial Intelligence, 2015.

Markdown

[Valls-Vargas et al. "Narrative Hermeneutic Circle: Improving Character Role Identification from Natural Language Text via Feedback Loops." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/vallsvargas2015ijcai-narrative/)

BibTeX

@inproceedings{vallsvargas2015ijcai-narrative,
  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 = {International Joint Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {2517-2523},
  url       = {https://mlanthology.org/ijcai/2015/vallsvargas2015ijcai-narrative/}
}