@inproceedings{hingmire-etal-2020-extracting,
title = "Extracting Message Sequence Charts from {H}indi Narrative Text",
author = "Hingmire, Swapnil and
Ramrakhiyani, Nitin and
Singh, Avinash Kumar and
Patil, Sangameshwar and
Palshikar, Girish and
Bhattacharyya, Pushpak and
Varma, Vasudeva",
editor = "Bonial, Claire and
Caselli, Tommaso and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Huang, Ruihong and
Iyyer, Mohit and
Jaimes, Alejandro and
Ji, Heng and
Martin, Lara J. and
Miller, Ben and
Mitamura, Teruko and
Peng, Nanyun and
Tetreault, Joel",
booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nuse-1.11",
doi = "10.18653/v1/2020.nuse-1.11",
pages = "87--96",
abstract = "In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi. An MSC is a formal representation allowing the depiction of actors and interactions among these actors in a scenario, apart from supporting a rich framework for formal inference. We propose an approach to extract MSC actors and interactions from a Hindi narrative. As a part of the approach, we enrich an existing event annotation scheme where we provide guidelines for annotation of the mood of events (realis vs irrealis) and guidelines for annotation of event arguments. We report performance on multiple evaluation criteria by experimenting with Hindi narratives from Indian History. Though Hindi is the fourth most-spoken first language in the world, from the NLP perspective it has comparatively lesser resources than English. Moreover, there is relatively less work in the context of event processing in Hindi. Hence, we believe that this work is among the initial works for Hindi event processing.",
}
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<abstract>In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi. An MSC is a formal representation allowing the depiction of actors and interactions among these actors in a scenario, apart from supporting a rich framework for formal inference. We propose an approach to extract MSC actors and interactions from a Hindi narrative. As a part of the approach, we enrich an existing event annotation scheme where we provide guidelines for annotation of the mood of events (realis vs irrealis) and guidelines for annotation of event arguments. We report performance on multiple evaluation criteria by experimenting with Hindi narratives from Indian History. Though Hindi is the fourth most-spoken first language in the world, from the NLP perspective it has comparatively lesser resources than English. Moreover, there is relatively less work in the context of event processing in Hindi. Hence, we believe that this work is among the initial works for Hindi event processing.</abstract>
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%0 Conference Proceedings
%T Extracting Message Sequence Charts from Hindi Narrative Text
%A Hingmire, Swapnil
%A Ramrakhiyani, Nitin
%A Singh, Avinash Kumar
%A Patil, Sangameshwar
%A Palshikar, Girish
%A Bhattacharyya, Pushpak
%A Varma, Vasudeva
%Y Bonial, Claire
%Y Caselli, Tommaso
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Huang, Ruihong
%Y Iyyer, Mohit
%Y Jaimes, Alejandro
%Y Ji, Heng
%Y Martin, Lara J.
%Y Miller, Ben
%Y Mitamura, Teruko
%Y Peng, Nanyun
%Y Tetreault, Joel
%S Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F hingmire-etal-2020-extracting
%X In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi. An MSC is a formal representation allowing the depiction of actors and interactions among these actors in a scenario, apart from supporting a rich framework for formal inference. We propose an approach to extract MSC actors and interactions from a Hindi narrative. As a part of the approach, we enrich an existing event annotation scheme where we provide guidelines for annotation of the mood of events (realis vs irrealis) and guidelines for annotation of event arguments. We report performance on multiple evaluation criteria by experimenting with Hindi narratives from Indian History. Though Hindi is the fourth most-spoken first language in the world, from the NLP perspective it has comparatively lesser resources than English. Moreover, there is relatively less work in the context of event processing in Hindi. Hence, we believe that this work is among the initial works for Hindi event processing.
%R 10.18653/v1/2020.nuse-1.11
%U https://aclanthology.org/2020.nuse-1.11
%U https://doi.org/10.18653/v1/2020.nuse-1.11
%P 87-96
Markdown (Informal)
[Extracting Message Sequence Charts from Hindi Narrative Text](https://aclanthology.org/2020.nuse-1.11) (Hingmire et al., NUSE-WNU 2020)
ACL
- Swapnil Hingmire, Nitin Ramrakhiyani, Avinash Kumar Singh, Sangameshwar Patil, Girish Palshikar, Pushpak Bhattacharyya, and Vasudeva Varma. 2020. Extracting Message Sequence Charts from Hindi Narrative Text. In Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, pages 87–96, Online. Association for Computational Linguistics.