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

• URL: http://arxiv.org/abs/2110.01349v1
• Date: Mon, 4 Oct 2021 11:54:43 GMT
• Title: Protagonists' Tagger in Literary Domain -- New Datasets and a Method for Person Entity Linkage
• Abstract summary: This research investigates the problem of detecting person entities and assigning them unique identities in novels. The protagonistTagger achieves both precision and recall of above 83% on the prepared testing sets. We gathered a corpus of 13 full-text novels tagged with protagonistTagger that comprises more than 35,000 mentions of literary characters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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