Extraction of Message Sequence Charts from Narrative History Text

Girish Palshikar, Sachin Pawar, Sangameshwar Patil, Swapnil Hingmire, Nitin Ramrakhiyani, Harsimran Bedi, Pushpak Bhattacharyya, Vasudeva Varma


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.
Anthology ID:
W19-2404
Volume:
Proceedings of the First Workshop on Narrative Understanding
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–36
Language:
URL:
https://aclanthology.org/W19-2404
DOI:
10.18653/v1/W19-2404
Bibkey:
Cite (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.
Cite (Informal):
Extraction of Message Sequence Charts from Narrative History Text (Palshikar et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2404.pdf