Using Functional Schemas to Understand Social Media Narratives

Yan, Xinru; Naik, Aakanksha; Jo, Yohan; Rosé, Carolyn P.

doi:10.18653/v1/w19-3403

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…Building upon this work, Yu et al (2023a) released a corpus of fiction and Wikipedia text to facilitate anaphoric reference discovery. Yan et al (2019) introduced a more complex structure called Functional Schema, which utilizes language models, to reflect how storytelling patterns make up the narrative. Mikhalkova et al (2020) introduces the Text World Theory (Werth, 1999;Wang et al, 2016) to regulate the structured annotations of narratives.…”

Section: Narrative Reading Comprehensionmentioning

confidence: 99%

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Zhu,

Zhao,

Gui

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author's imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension.

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Section: Narrative Reading Comprehensionmentioning

confidence: 99%

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Zhu,

Zhao,

Gui

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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“…As NLP datasets, narratives are often directly collected by sampling data from sources that are already known to use narrative based on the genre of the corpus, such as literary works (Hammond et al, 2013), doctors' notes (Elhadad et al, 2015), or fan fiction (Yoder et al, 2021). In the social media domain, data is often sampled in a way to ensure the presence of narratives, e.g., by collecting posts from specific subreddits which typically contain narrative style posts (Yan et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 4th Workshop of Narrative Understanding (WNU2022)

2022

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Welcome to the 4th Workshop on Narrative Understanding! This is the 4th iteration of the workshop, which brings together an interdisciplinary group of researchers from AI, ML, NLP, Computer Vision and other related fields, as well as scholars from the humanities to discuss methods to improve automatic narrative understanding capabilities. We are happy to present 8 papers on this topic (along with 7 non-archival papers to be presented only at the workshop). These papers take on the complex challenges presented by diverse texts in areas of film, dialogue and literature as they look to improve methods for event extraction, gender and representation bias, controllable generation, quality assessment, and other tasks related to the workshop theme. We would like to thank everyone who submitted their work to this workshop and the program committee for their helpful feedback.

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Section: Related Workmentioning

confidence: 99%

Narrative Detection and Feature Analysis in Online Health Communities

Ganti¹,

Wilson²,

Ma³

et al. 2022

Proceedings of the 4th Workshop of Narrative Understanding (WNU2022)

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Narratives have been shown to be an effective way to communicate health risks and promote health behavior change, and given the growing amount of health information being shared on social media, it is crucial to study healthrelated narratives in social media. However, expert identification of a large number of narrative texts is a time consuming process, and larger scale studies on the use of narratives may be enabled through automatic text classification approaches. Prior work has demonstrated that automatic narrative detection is possible, but modern deep learning approaches have not been used for this task in the domain of online health communities. Therefore, in this paper, we explore the use of deep learning methods to automatically classify the presence of narratives in social media posts, finding that they outperform previously proposed approaches. We also find that in many cases, these models generalize well across posts from different health organizations. Finally, in order to better understand the increase in performance achieved by deep learning models, we use feature analysis techniques to explore the features that most contribute to narrative detection for posts in online health communities.

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Using Functional Schemas to Understand Social Media Narratives

Cited by 8 publications

References 39 publications

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Proceedings of the 4th Workshop of Narrative Understanding (WNU2022)

Narrative Detection and Feature Analysis in Online Health Communities

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