Evidentials in causal premise semantics

theoretical and experimental investigation

Yurie Hara, Naho Orita, Hiromu Sakai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We formalize the causal component of Davis & Hara’s (2014) analysis of Japanese evidentiality, which defines “indirect evidence” as an observation of the effect state of the cause-effect dependency. The analysis correctly predicts that uttering p-youda only commits the speaker to ‘if p, q must be true’ but not to the prejacent p, and successfully derives the asymmetry between the prejacent p and the evidence source q. Also, the results of the rating study and the corpus study show that the interpretation and the distribution of evidentials are subject to the cause-effect dependencies.

Original languageEnglish
Title of host publicationNew Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers
EditorsKoji Mineshima, Kazuhiro Kojima, Ken Satoh, Sachiyo Arai, Daisuke Bekki, Yuiko Ohta
PublisherSpringer-Verlag
Pages282-298
Number of pages17
ISBN (Print)9783319937939
DOIs
Publication statusPublished - 2018 Jan 1
Event9th JSAI International Symposium on Artificial Intelligence, JSAI-isAI 2017 - Tsukuba, Japan
Duration: 2017 Nov 132017 Nov 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10838 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th JSAI International Symposium on Artificial Intelligence, JSAI-isAI 2017
CountryJapan
CityTsukuba
Period17/11/1317/11/15

Fingerprint

Experimental Investigation
Semantics
Asymmetry
Predict
Evidence

Keywords

  • Causal network
  • Causal premise semantics
  • Causality
  • Corpus study
  • Evidentiality
  • Implicature
  • Modality
  • Naturalness rating experiment

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hara, Y., Orita, N., & Sakai, H. (2018). Evidentials in causal premise semantics: theoretical and experimental investigation. In K. Mineshima, K. Kojima, K. Satoh, S. Arai, D. Bekki, & Y. Ohta (Eds.), New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers (pp. 282-298). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10838 LNAI). Springer-Verlag. https://doi.org/10.1007/978-3-319-93794-6_20

Evidentials in causal premise semantics : theoretical and experimental investigation. / Hara, Yurie; Orita, Naho; Sakai, Hiromu.

New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers. ed. / Koji Mineshima; Kazuhiro Kojima; Ken Satoh; Sachiyo Arai; Daisuke Bekki; Yuiko Ohta. Springer-Verlag, 2018. p. 282-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10838 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hara, Y, Orita, N & Sakai, H 2018, Evidentials in causal premise semantics: theoretical and experimental investigation. in K Mineshima, K Kojima, K Satoh, S Arai, D Bekki & Y Ohta (eds), New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10838 LNAI, Springer-Verlag, pp. 282-298, 9th JSAI International Symposium on Artificial Intelligence, JSAI-isAI 2017, Tsukuba, Japan, 17/11/13. https://doi.org/10.1007/978-3-319-93794-6_20
Hara Y, Orita N, Sakai H. Evidentials in causal premise semantics: theoretical and experimental investigation. In Mineshima K, Kojima K, Satoh K, Arai S, Bekki D, Ohta Y, editors, New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers. Springer-Verlag. 2018. p. 282-298. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-93794-6_20
Hara, Yurie ; Orita, Naho ; Sakai, Hiromu. / Evidentials in causal premise semantics : theoretical and experimental investigation. New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers. editor / Koji Mineshima ; Kazuhiro Kojima ; Ken Satoh ; Sachiyo Arai ; Daisuke Bekki ; Yuiko Ohta. Springer-Verlag, 2018. pp. 282-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{12f45bbb0cab4033b48f786564a47c11,
title = "Evidentials in causal premise semantics: theoretical and experimental investigation",
abstract = "We formalize the causal component of Davis & Hara’s (2014) analysis of Japanese evidentiality, which defines “indirect evidence” as an observation of the effect state of the cause-effect dependency. The analysis correctly predicts that uttering p-youda only commits the speaker to ‘if p, q must be true’ but not to the prejacent p, and successfully derives the asymmetry between the prejacent p and the evidence source q. Also, the results of the rating study and the corpus study show that the interpretation and the distribution of evidentials are subject to the cause-effect dependencies.",
keywords = "Causal network, Causal premise semantics, Causality, Corpus study, Evidentiality, Implicature, Modality, Naturalness rating experiment",
author = "Yurie Hara and Naho Orita and Hiromu Sakai",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-93794-6_20",
language = "English",
isbn = "9783319937939",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "282--298",
editor = "Koji Mineshima and Kazuhiro Kojima and Ken Satoh and Sachiyo Arai and Daisuke Bekki and Yuiko Ohta",
booktitle = "New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers",

}

TY - GEN

T1 - Evidentials in causal premise semantics

T2 - theoretical and experimental investigation

AU - Hara, Yurie

AU - Orita, Naho

AU - Sakai, Hiromu

PY - 2018/1/1

Y1 - 2018/1/1

N2 - We formalize the causal component of Davis & Hara’s (2014) analysis of Japanese evidentiality, which defines “indirect evidence” as an observation of the effect state of the cause-effect dependency. The analysis correctly predicts that uttering p-youda only commits the speaker to ‘if p, q must be true’ but not to the prejacent p, and successfully derives the asymmetry between the prejacent p and the evidence source q. Also, the results of the rating study and the corpus study show that the interpretation and the distribution of evidentials are subject to the cause-effect dependencies.

AB - We formalize the causal component of Davis & Hara’s (2014) analysis of Japanese evidentiality, which defines “indirect evidence” as an observation of the effect state of the cause-effect dependency. The analysis correctly predicts that uttering p-youda only commits the speaker to ‘if p, q must be true’ but not to the prejacent p, and successfully derives the asymmetry between the prejacent p and the evidence source q. Also, the results of the rating study and the corpus study show that the interpretation and the distribution of evidentials are subject to the cause-effect dependencies.

KW - Causal network

KW - Causal premise semantics

KW - Causality

KW - Corpus study

KW - Evidentiality

KW - Implicature

KW - Modality

KW - Naturalness rating experiment

UR - http://www.scopus.com/inward/record.url?scp=85049698187&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049698187&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-93794-6_20

DO - 10.1007/978-3-319-93794-6_20

M3 - Conference contribution

SN - 9783319937939

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 282

EP - 298

BT - New Frontiers in Artificial Intelligence - JSAI-isAI Workshops, JURISIN, SKL, AI-Biz, LENLS, AAA, SCIDOCA, kNeXI, Revised Selected Papers

A2 - Mineshima, Koji

A2 - Kojima, Kazuhiro

A2 - Satoh, Ken

A2 - Arai, Sachiyo

A2 - Bekki, Daisuke

A2 - Ohta, Yuiko

PB - Springer-Verlag

ER -