• Corpus ID: 213182690

Temporal Embeddings and Transformer Models for Narrative Text Understanding

@inproceedings{Vani2020TemporalEA,
  title={Temporal Embeddings and Transformer Models for Narrative Text Understanding},
  author={K. Vani and Simone Mellace and Alessandro Antonucci},
  booktitle={Text2Story@ECIR},
  year={2020}
}
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… 

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