# Protagonists' Tagger in Literary Domain -- New Datasets and a Method for Person Entity Linkage

arXiv: https://arxiv.org/abs/2110.01349 · HTML: https://mlsanity.com/paper/2110.01349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Maxout Networks](https://mlsanity.com/paper/1302.4389/) (2013)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [A Survey on Sentiment and Emotion Analysis for Computational Literary Studies](https://mlsanity.com/paper/1808.03137/) (2018)
- [Named Entity Recognition -- Is there a glass ceiling?](https://mlsanity.com/paper/1910.02403/) (2019)
- [On the Strength of Character Language Models for Multilingual Named Entity Recognition](https://mlsanity.com/paper/1809.05157/) (2018)

## Abstract (arXiv)

Semantic annotation of long texts, such as novels, remains an open challenge in Natural Language Processing (NLP). This research investigates the problem of detecting person entities and assigning them unique identities, i.e., recognizing people (especially main characters) in novels. We prepared a method for person entity linkage (named entity recognition and disambiguation) and new testing datasets. The datasets comprise 1,300 sentences from 13 classic novels of different genres that a novel reader had manually annotated. Our process of identifying literary characters in a text, implemented in protagonistTagger, comprises two stages: (1) named entity recognition (NER) of persons, (2) named entity disambiguation (NED) - matching each recognized person with the literary character's full name, based on approximate text matching. The protagonistTagger achieves both precision and recall of above 83% on the prepared testing sets. Finally, we gathered a corpus of 13 full-text novels tagged with protagonistTagger that comprises more than 35,000 mentions of literary characters.