@inproceedings{palshikar-etal-2019-extraction-message,
title = "Extraction of Message Sequence Charts from Narrative History Text",
author = "Palshikar, Girish and
Pawar, Sachin and
Patil, Sangameshwar and
Hingmire, Swapnil and
Ramrakhiyani, Nitin and
Bedi, Harsimran and
Bhattacharyya, Pushpak and
Varma, Vasudeva",
editor = "Bamman, David and
Chaturvedi, Snigdha and
Clark, Elizabeth and
Fiterau, Madalina and
Iyyer, Mohit",
booktitle = "Proceedings of the First Workshop on Narrative Understanding",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2404",
doi = "10.18653/v1/W19-2404",
pages = "28--36",
abstract = "In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering. We propose algorithms to automatically extract an MSC from a history narrative. For a given narrative, we first identify verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. As a final step in MSC extraction, we employ a state-of-the art algorithm to temporally re-order these interactions. Our evaluation on multiple publicly available narratives shows improvements over four baselines.",
}
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<abstract>In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering. We propose algorithms to automatically extract an MSC from a history narrative. For a given narrative, we first identify verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. As a final step in MSC extraction, we employ a state-of-the art algorithm to temporally re-order these interactions. Our evaluation on multiple publicly available narratives shows improvements over four baselines.</abstract>
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%0 Conference Proceedings
%T Extraction of Message Sequence Charts from Narrative History Text
%A Palshikar, Girish
%A Pawar, Sachin
%A Patil, Sangameshwar
%A Hingmire, Swapnil
%A Ramrakhiyani, Nitin
%A Bedi, Harsimran
%A Bhattacharyya, Pushpak
%A Varma, Vasudeva
%Y Bamman, David
%Y Chaturvedi, Snigdha
%Y Clark, Elizabeth
%Y Fiterau, Madalina
%Y Iyyer, Mohit
%S Proceedings of the First Workshop on Narrative Understanding
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F palshikar-etal-2019-extraction-message
%X In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering. We propose algorithms to automatically extract an MSC from a history narrative. For a given narrative, we first identify verbs which indicate interactions and then use dependency parsing and Semantic Role Labelling based approaches to identify senders (initiating actors) and receivers (other actors involved) for these interaction verbs. As a final step in MSC extraction, we employ a state-of-the art algorithm to temporally re-order these interactions. Our evaluation on multiple publicly available narratives shows improvements over four baselines.
%R 10.18653/v1/W19-2404
%U https://aclanthology.org/W19-2404
%U https://doi.org/10.18653/v1/W19-2404
%P 28-36
Markdown (Informal)
[Extraction of Message Sequence Charts from Narrative History Text](https://aclanthology.org/W19-2404) (Palshikar et al., WNU 2019)
ACL
- Girish Palshikar, Sachin Pawar, Sangameshwar Patil, Swapnil Hingmire, Nitin Ramrakhiyani, Harsimran Bedi, Pushpak Bhattacharyya, and Vasudeva Varma. 2019. Extraction of Message Sequence Charts from Narrative History Text. In Proceedings of the First Workshop on Narrative Understanding, pages 28–36, Minneapolis, Minnesota. Association for Computational Linguistics.