Protagonists' Tagger in Literary Domain -- New Datasets and a Method for Person Entity Linkage
This addresses the challenge of semantic annotation in long literary texts for NLP applications, but it is incremental as it builds on existing NER and NED methods.
The paper tackled the problem of detecting and linking person entities in novels, achieving precision and recall above 83% on new datasets of 1,300 sentences from 13 classic novels.
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.