Temporal Embeddings and Transformer Models for Narrative Text Understanding
| Autor: | K, Vani, Mellace, Simone, Antonucci, Alessandro |
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| Rok vydání: | 2020 |
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| Druh dokumentu: | Working Paper |
| Popis: | We present two deep learning approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time. An empirical analysis of the corresponding character trajectories shows that such approaches are effective in depicting dynamic evolution. A supervised learning approach based on the state-of-the-art transformer model BERT is used instead to detect static relations between characters. The empirical validation shows that such events (e.g., two characters belonging to the same family) might be spotted with good accuracy, even when using automatically annotated data. This provides a deeper understanding of narrative plots based on the identification of key facts. Standard clustering techniques are finally used for character de-aliasing, a necessary pre-processing step for both approaches. Overall, deep learning models appear to be suitable for narrative text understanding, while also providing a challenging and unexploited benchmark for general natural language understanding. Comment: Presented at the Third International Workshop on Narrative Extraction from Texts (Text2Story 2020) held in conjunction with the 42nd European Conference on Information Retrieval |
| Databáze: | arXiv |
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