Abstract
Event models, in the form of scripts, frames, or precondition/effect axioms, allow for reasoning about the causal and motivational connections between events in a story, and thus are central to AI understanding and generating narratives. However, previous efforts to learn general structured event models from text have overlooked important challenges raised by the narrative text, such as the complex (nested) event arguments and inferring the order and actuality of mentioned events. We present an NLP pipeline for extracting (partially) ordered, structured event representations for use in learning general event models from three large text corpora. We address each of the challenges that we identify to some degree, but also conclude that they raise open problems for future research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We will use the terms event and action interchangeably. Although a distinction can be made – actions have at least one intentional participant or actor – from the point of view of the models we consider, they are largely the same.
- 2.
- 3.
References
Aldawsari, M., Finlayson, M.A.: Detecting subevents using discourse and narrative features. In: Proceedings of the 57th Annual Meeting of ACL (2019)
Arora, A., Fiorino, H., Pellier, D., Métivier, M., Pesty, S.: A review of learning planning action models. Knowl. Eng. Rev. 33 (2018)
Bamman, D., O’Connor, B., Smith, N.A.: Learning latent personas of film characters. In: Proceedings of ACL, pp. 352–361 (2013)
Bekoulis, G., Deleu, J., Demeester, T., Develder, C.: Sub-event detection from Twitter streams as a sequence labeling problem. arXiv preprint arXiv:1903.05396 (2019)
Bethard, S.: ClearTK-timeML: a minimalist approach to TempEval 2013. In: Proceedings of SemEval 2013, pp. 10–14 (2013)
Biten, A.F., Gomez, L., Rusinol, M., Karatzas, D.: Good news, everyone! context driven entity-aware captioning for news images. In: CVPR (2019)
Bonial, C., Hwang, J., Bonn, J., Conger, K., Babko-Malaya, O., Palmer, M.: English PropBank annotation guidelines. Center for Computational Language and Education Research Institute of Cognitive Science University of Colorado (2012)
Callanan, E., De Venezia, R., Armstrong, V., Paredes, A., Chakraborti, T., Muise, C.: MACQ: a holistic view of model acquisition techniques. arXiv preprint arXiv:2206.06530 (2022)
Chambers, N., Jurafsky, D.: Unsupervised learning of narrative event chains. In: Proceeding of ACL (2008)
Chambers, N., Jurafsky, D.: Unsupervised learning of narrative schemas and their participants. In: Proceedings 47th ACL Meeting and 4th IJCNLP, pp. 602–610 (2009)
Cook, W.W.: Plotto: The Master Book of All Plots. Tin House Books (1928)
Cresswell, S., Gregory, P.: Generalised domain model acquisition from action traces. In: Twenty-First ICAPS (2011)
Feng, W., Zhuo, H.H., Kambhampati, S.: Extracting action sequences from texts based on deep reinforcement learning. In: Proceedings of IJCAI, pp. 4064–4070 (2018)
Fischbach, J., et al.: Automatic detection of causality in requirement artifacts: the CiRA approach. In: Dalpiaz, F., Spoletini, P. (eds.) REFSQ 2021. LNCS, vol. 12685, pp. 19–36. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73128-1_2
Geffner, H., Bonet, B.: A Concise Introduction to Models and Methods for Automated Planning. Morgan & Claypool (2013). ISBN 9781608459698
Glavaš, G., Šnajder, J., Kordjamshidi, P., Moens, M.F.: HiEve: a corpus for extracting event hierarchies from news stories. In: Proceedings of 9th Language Resources and Evaluation Conference, pp. 3678–3683. ELRA (2014)
Hayton, T., Porteous, J., Ferreira, J., Lindsay, A., Read, J.: StoryFramer: from input stories to output planning models. In: ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (2017)
Hayton, T., Porteous, J., Ferreira, J.F., Lindsay, A.: Narrative planning model acquisition from text summaries and descriptions. In: Proceedings of AAAI (2020)
Komai, M., Shindo, H., Matsumoto, Y.: An efficient annotation for phrasal verbs using dependency information. In: Proceedings of PACLIC, pp. 125–131 (2015)
Lamperti, G., Zanella, M.: Diagnosis of active systems (2003)
Laokulrat, N., Miwa, M., Tsuruoka, Y., Chikayama, T.: Uttime: temporal relation classification using deep syntactic features. In: SemEval, pp. 88–92 (2013)
Lee, K., He, L., Zettlemoyer, L.: Higher-order coreference resolution with coarse-to-fine inference. arXiv preprint arXiv:1804.05392 (2018)
Lindsay, A., Read, J., Ferreira, J., Hayton, T., Porteous, J., Gregory, P.: Framer: planning models from natural language action descriptions. In: ICAPS (2017)
Manikonda, L., Sohrabi, S., Talamadupula, K., Srivastava, B., Kambhampati, S.: Extracting incomplete planning action models from unstructured social media data to support decision making. In: KEPS (2017)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: ACL (2014)
McDermott, D., et al.: PDDL-the planning domain definition language–version 1.2 (1998)
Miglani, S., Yorke-Smith, N.: NLtoPDDL: one-shot learning of PDDL models from natural language process manuals. In: KEPS (2020)
Mirza, P., Sprugnoli, R., Tonelli, S., Speranza, M.: Annotating causality in the TempEval-3 corpus. In: CAtoCL, pp. 10–19 (2014)
Mirza, P., Tonelli, S.: CATENA: CAusal and TEmporal relation extraction from NAtural language texts. In: Proceedings of COLING, pp. 64–75 (2016)
Mostafazadeh, N., et al.: A corpus and cloze evaluation for deeper understanding of commonsense stories. In: Proceedings of NAACL, pp. 839–849 (2016)
Ning, Q., Subramanian, S., Roth, D.: An improved neural baseline for temporal relation extraction. In: Proceedings of EMNLP, pp. 6203–6209 (2019)
Ning, Q., Wu, H., Roth, D.: A multi-axis annotation scheme for event temporal relations. In: ACL (2018). http://cogcomp.org/papers/NingWuRo18.pdf
Olmo, A., Sreedharan, S., Kambhampati, S.: GPT3-to-plan: extracting plans from text using GPT-3. arXiv preprint arXiv:2106.07131 (2021)
Puente, C., Sobrino, A., Olivas, J.A., Merlo, R.: Extraction, analysis and representation of imperfect conditional and causal sentences by means of a semi-automatic process. In: International Conference on Fuzzy Systems, pp. 1–8. IEEE (2010)
Saurí, R., Littman, J., Knippen, B., Gaizauskas, R., Setzer, A., Pustejovsky, J.: TimeML Annotation Guidelines Version 1.2.1 (2006)
Sil, A., Yates, A.: Extracting STRIPS representations of actions and events. In: Recent Advances in Natural Language Processing (2011)
Tan, F.A., et al.: The causal news corpus: annotating causal relations in event sentences from news. arXiv preprint arXiv:2204.11714 (2022)
Tandon, N., de Melo, G., De, A., Weikum, G.: Knowlywood: mining activity knowledge from Hollywood narratives. In: Proceedings of the CIKM (2015)
Van Harmelen, F., Lifschitz, V., Porter, B.: Handbook of Knowledge Representation. Elsevier (2008)
Webber, B., Prasad, R., Lee, A., Joshi, A.: The Penn discourse treebank 3.0 annotation manual. Philadelphia, University of Pennsylvania 35, 108 (2019)
Yang, Q., Wu, K., Jiang, Y.: Learning action models from plan examples using weighted MAX-SAT. Artif. Intell. 171(2–3), 107–143 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, R., Haslum, P., Cui, L. (2024). Towards Learning Action Models from Narrative Text Through Extraction and Ordering of Structured Events. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14472. Springer, Singapore. https://doi.org/10.1007/978-981-99-8391-9_2
Download citation
DOI: https://doi.org/10.1007/978-981-99-8391-9_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8390-2
Online ISBN: 978-981-99-8391-9
eBook Packages: Computer ScienceComputer Science (R0)