# Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature

arXiv: https://arxiv.org/abs/2508.03358 · HTML: https://mlsanity.com/paper/2508.03358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [Inferring Interpersonal Relations in Narrative Summaries](https://mlsanity.com/paper/1512.00112/) (2015)
- [Extraction and Analysis of Fictional Character Networks: A Survey](https://mlsanity.com/paper/1907.02704/) (2019)
- [Mapping Out Narrative Structures and Dynamics Using Networks and Textual Information](https://mlsanity.com/paper/1604.03029/) (2016)
- [An Automated Pipeline for Character and Relationship Extraction from Readers' Literary Book Reviews on Goodreads.com](https://mlsanity.com/paper/2004.09601/) (2020)
- [Interconnected Kingdoms: Comparing 'A Song of Ice and Fire' Adaptations Across Media Using Complex Networks](https://mlsanity.com/paper/2410.05453/) (2024)

## Abstract (arXiv)

Automatically identifying characters and their interactions from fiction books is, arguably, a complex task that requires pipelines that leverage multiple Natural Language Processing (NLP) methods, such as Named Entity Recognition (NER) and Part-of-speech (POS) tagging. However, these methods are not optimized for the task that leads to the construction of Social Networks of Characters. Indeed, the currently available methods tend to underperform, especially in less-represented languages, due to a lack of manually annotated data for training. Here, we propose a pipeline, which we call Taggus, to extract social networks from literary fiction works in Portuguese. Our results show that compared to readily available State-of-the-Art tools -- off-the-shelf NER tools and Large Language Models (ChatGPT) -- the resulting pipeline, which uses POS tagging and a combination of heuristics, achieves satisfying results with an average F1-Score of 94.1% in the task of identifying characters and solving for co-reference and 75.9% in interaction detection. These represent, respectively, an increase of 50.7% and 22.3% on results achieved by the readily available State-of-the-Art tools. Further steps to improve results are outlined, such as solutions for detecting relationships between characters. Limitations on the size and scope of our testing samples are acknowledged. The Taggus pipeline is publicly available to encourage development in this field for the Portuguese language.2