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Improving Scholarly Knowledge Representation: Evaluating BERT-Based Models for Scientific Relation Classification Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Unchiku Generation Using Narrative Explanation Mechanism

Authors : Jumpei Ono, Takashi Ogata

Published in: Digital Libraries at Times of Massive Societal Transition

Publisher: Springer International Publishing

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Abstract

In this paper, the authors propose an “unchiku generation mechanism” they have developed to create deep rhetorical structures (narrative discourses) in a narrative. The system uses a mechanism that can generate unchiku, which refers to the detailed and excessive knowledge regarding a specific object, theme, or topic. In particular, the attribute information of each noun concept is automatically extracted from the Japanese Wikipedia and stored in the noun conceptual dictionary of an integrated narrative generation system. The proposed generation mechanism enables the generation of unchiku information related to various objects and topics by inserting parts of the extracted unchiku knowledge content into various points in the story created by the integrated narrative generation system. The attribute information related to Kabuki is derived from the Japanese Wikipedia and utilized by the formulated unchiku generation mechanism.

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previous chapter Using Deep Learning to Recognize Handwritten Thai Noi Characters in Ancient Palm Leaf Manuscripts
next chapter Analyzing the Stage Performance Structure of a Kabuki-Dance, Kyoganoko Musume Dojoji, Using an Animation System
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Kawai, M., Ono, J., Ogata, T.: Analyzing the stage performance structure of kabuki-dance “Kyoganoko Musume Dojoji.” In: Proceedings of the 34th Annual Conference of the Japanese Society of Artificial Intelligence, 3D1-OS-22a-04. Japanese Society of Artificial Intelligence, Tokyo (2020)
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Metadata
Title
Unchiku Generation Using Narrative Explanation Mechanism
Authors
Jumpei Ono
Takashi Ogata
Copyright Year
2020
Book
Digital Libraries at Times of Massive Societal Transition

Print ISBN: 978-3-030-64451-2

Electronic ISBN: 978-3-030-64452-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-64452-9_21

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