Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)
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| Challenge: | Existing literature analysis does not focus on roles of characters or on relationships between them. |
| Approach: | They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts. |
| Outcome: | The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph. |
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| Challenge: | Emotion analysis and classification is a challenging task which has been tackled with relatively straight-forward approaches. |
| Approach: | They propose to annotate emotion trigger phrases and entities in the roles of experiencers, targets, and causes of the emotion in literature by Project Gutenberg. |
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An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus (2020.lrec-1)
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| Challenge: | Existing frameworks for emotion recognition are limited and do not allow for categorical versus dimensional oppositions. |
| Approach: | They propose to use the emotions joy, love, anger, sadness and fear as well as dimensional models to annotate texts from different domains and topics. |
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EmotionLines: An Emotion Corpus of Multi-Party Conversations (L18-1)
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| Challenge: | Emotion is a critical characteristic to distinguish people from machines. |
| Approach: | They propose a dataset with emotions labeling on all utterances in each dialogue . they use Friends TV scripts and Facebook messenger dialogues to collect the data . |
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Learning and Evaluating Character Representations in Novels (2022.findings-acl)
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| Challenge: | Recent advances in word embeddings have proven successful in learning entity representations from short texts but do not capture full book-level information. |
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Guilt by Association: Emotion Intensities in Lexical Representations (2021.emnlp-main)
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| Challenge: | linguistic models have a higher correlation with human ground truth ratings than labeled data . word vectors have often been evaluated on standard word relatedness benchmarks . |
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A Straightforward Approach to Narratologically Grounded Character Identification (2020.coling-main)
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| Challenge: | Existing definitions of character are based on simplified or implicit definitions that do not capture essential distinctions between characters and other referents in narratives. |
| Approach: | They propose a narratologically grounded definition of character that is based on clear narrological principles and annotated 170 narrative texts. |
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Learning to Relate from Captions and Bounding Boxes (P19-1)
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| Challenge: | Existing methods for classifying images without supervision are limited. |
| Approach: | They propose a top-down attention mechanism to align entities in captions to objects in the image and leverage the syntactic structure of captions for alignment. |
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Modeling Naive Psychology of Characters in Simple Commonsense Stories (P18-1)
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| Challenge: | Understanding a narrative requires reasoning about the causal links between the events in the story and the mental states of the characters, even when those relationships are not explicitly stated. |
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Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
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Personality Understanding of Fictional Characters during Book Reading (2023.acl-long)
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| Challenge: | Existing methods to predict characters' personalities have not been studied in the NLP field due to the lack of appropriate datasets mimicking the process of book reading. |
| Approach: | They propose a dataset to predict characters' personalities that uses an exhaustive vocabulary of personality traits as targets. |
| Outcome: | The proposed dataset is efficient and accurate and relies on long-term context to achieve accurate predictions for both machines and humans. |