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LISA: Lexically Intelligent Story Assistant
Open accessAbstract
This paper serves as an introduction to building an assistive tool for story writers. Our tool, Lexically Intelligent Story Assistant (or LISA), aims to assist story writers by providing real-time feedback on lexical inconsistencies in the story. LISA analyzes the narrative and builds a knowledge base, using artificial intelligence to make inferences and point out errors in the narrative. Moreover, it also allows the user to interact with the system by querying the knowledge base in natural language form. This tool shows that it is possible to create a database for a narrative and use artificial intelligence to improve authoring of stories.
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“Previous research demonstrates that AI can support screenwriters by summarizing and cross-checking content to ensure the consistency of screenplay text [44, 84]”
From Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation (2025) · cites this paper as [84]
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“Additionally, while existing tools leverage big data for screenwriting and feedback analysis [72, 77, 84]”
From Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation (2025) · cites this paper as [84]
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Cites
Writing Stories with Help from Recurrent Neural Networks
Roemmele, M. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9810 · Cited by 59
Research works printed but not linked: 24
- Angeli, G.; Premkumar, M. J.; and Manning, C. D. 2015. Leveraging linguistic structure for open domain information extraction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015).
- Bhattacharya, P., and Neamtiu, I. 2011. A prolog-based framework for search, integration and empirical analysis on software evolution data. In Proceedings of the 3rd Interna- tional Workshop on Search-Driven Development: Users, In- frastructure, Tools, and Evaluation, SUITE '11, 29-32. New York, NY, USA: ACM.
- Carlson, L.; Marcu, D.; and Okurowski, M. E. 2003. Build- ing a discourse-tagged corpus in the framework of rhetorical structure theory. In Current and new directions in discourse and dialogue. Springer. 85-112.
- Chen, W., and Fahlman, S. E. 2008. Modeling mental con- texts and their interactions. In AAAI Fall Symposium: Bio- logically Inspired Cognitive Architectures, volume 8, 04.
- Chen, D., and Manning, C. D. 2014. A fast and accurate dependency parser using neural networks. In EMNLP, 740- 750.
- Elson, D. 2012. Modelling Narrative Discourse. Ph.D. Dissertation, Columbia University.
- Ginsberg, A. 1988. Knowledge-base reduction: A new ap- proach to checking knowledge bases for inconsistency and redundancy. In AAAI, volume 88, 21-26.
- Hayes-Roth, F. 1985. Rule-based systems. Commun. ACM 28(9):921-932.
- Kapadia, M.; Falk, J.; Zünd, F.; Marti, M.; Sumner, R. W.; and Gross, M. 2015. Computer-assisted authoring of inter- active narratives. In Proceedings of the 19th Symposium on Interactive 3D Graphics and Games, i3D '15, 85-92. New York, NY, USA: ACM.
- Kapadia, M.; Frey, S.; Shoulson, A.; Sumner, R. W.; and Gross, M. 2016. Canvas: Computer-assisted narrative animation synthesis. In Proceedings of the ACM SIG- GRAPH/Eurographics Symposium on Computer Animation, SCA '16, 199-209.
- Laffey, T. J.; Cox, P. A.; Schmidt, J. L.; Kao, S. M.; and Readk, J. Y. 1988. Real-time knowledge-based systems. AI magazine 9(1):27.
- Lee, H.; Peirsman, Y.; Chang, A.; Chambers, N.; Surdeanu, M.; and Jurafsky, D. 2011. Stanford's multi-pass sieve coref- erence resolution system at the conll-2011 shared task. In Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, 28-34. Associa- tion for Computational Linguistics.
- Lee, H.; Chang, A.; Peirsman, Y.; Chambers, N.; Surdeanu, M.; and Jurafsky, D. 2013. Deterministic coreference reso- lution based on entity-centric, precision-ranked rules. Com- putational Linguistics 39(4):885-916.
- Manning, C. D.; Surdeanu, M.; Bauer, J.; Finkel, J.; Inc, P.; Bethard, S. J.; and Mcclosky, D. 2014. The stanford corenlp natural language processing toolkit. In In ACL, Sys- tem Demonstrations.
- Mateas, M., and Stern, A. 2003. Fac ¸ade: An experiment in building a fully-realized interactive drama. In Game devel- opers conference, volume 2.
- Mateas, M., and Stern, A. 2004. A behavior language: Joint action and behavioral idioms. In Prendinger, H., and Ishizuka, M., eds., Life-Like Characters, Cognitive Tech- nologies. Springer Berlin Heidelberg. 135-161.
- Miller, G. A. 1995. Wordnet: A lexical database for english. COMMUNICATIONS OF THE ACM 38:39-41.
- O'Leary, D. E. 1998. Using ai in knowledge management: Knowledge bases and ontologies. IEEE Intelligent Systems and Their Applications 13(3):34-39.
- Schuler, K. K. 2005. Verbnet: A Broad-coverage, Compre- hensive Verb Lexicon. Ph.D. Dissertation, Philadelphia, PA, USA. AAI3179808.
- Shoulson, A.; Gilbert, M. L.; Kapadia, M.; and Badler, N. I. 2013. An event-centric planning approach for dynamic real- time narrative. In Proceedings of Motion on Games, 121- 130. ACM.
- SMITH, T. C., and WITTEN, I. H. 1991. A planning mecha- nism for generating story text. Literary and Linguistic Com- puting 6(2):119-126.
- Suwa, M.; Scott, A. C.; and Shortliffe, E. H. 1982. An ap- proach to verifying completeness and consistency in a rule- based expert system. Ai Magazine 3(4):16.
- Toutanova, K.; Klein, D.; Manning, C. D.; and Singer, Y. 2003. Feature-rich part-of-speech tagging with a cyclic de- pendency network. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Com- putational Linguistics on Human Language Technology- Volume 1, 173-180. Association for Computational Linguis- tics.
- Valls-Vargas, J.; Zhu, J.; and Ontanon, S. 2016. Error analysis in an automated narrative information extraction pipeline. IEEE Transactions on Computational Intelligence and AI in Games PP(99):1-1.
Cited by
Sorted by citations
Sketching NLP: A Case Study of Exploring the Right Things To Design with Language Intelligence
Yang, Q., Cranshaw, J., Amershi, S., Iqbal, S. T., & Teevan, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300415 · Cited by 90
Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation
Tang, Y., Li, H., Lan, M., Ma, X., & Qu, H. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714120 · Cited by 18
How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of Screenwriters
Tang, Y., Zhou, J., Li, H., Xie, X., Ma, X., & Qu, H. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790300 · Cited by 5
“The increasing integration of AI in screenwriting transforms not only the quality and style of final outputs [43, 46] but also the practices through which story ideas are conceived and developed [31, 32, 54, 63].”
How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of Screenwriters
Tang, Y., Zhou, J., Li, H., Xie, X., Ma, X., & Qu, H. (2026) · arXiv · Cited by 5
Metadata
- Authors
- Sanghrajka, R., Hidalgo, D., Chen, P., & Kapadia, M.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
- Published
- 2017-10-09
- Volume
- 13
- Issue
- 1
- Pages
- 221-227
- ISSN
- 2326-909X (ISSN-L)
- 2334-0924
- OpenAlex
- W2760428132
- Cited by
- 10
- References
- 1
- Citations by year
- 2025: 1 · 2021: 1 · 2019: 5 · 2018: 3
- FWCI
- 1.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- real-time feedback, knowledge base construction, natural language querying
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- Sanghrajka, R., Hidalgo, D., Chen, P., & Kapadia, M. (2017). LISA: Lexically Intelligent Story Assistant. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. https://doi.org/10.1609/aiide.v13i1.12956
