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Interactive Ambiguity Resolution of Named Entities in Fictional Literature
Author(s) -
Stoffel Florian,
Jentner Wolfgang,
Behrisch Michael,
Fuchs Johannes,
Keim Daniel
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13179
Subject(s) - computer science , ambiguity , ambiguity resolution , context (archaeology) , named entity recognition , software , process (computing) , entity linking , resolution (logic) , natural language processing , information retrieval , iterative and incremental development , task (project management) , artificial intelligence , programming language , software engineering , knowledge base , telecommunications , paleontology , management , gnss applications , global positioning system , economics , biology
Named entity recognition (NER) denotes the task to detect entities and their corresponding classes, such as person or location, in unstructured text data. For most applications, state of the art NER software is producing reasonable results. However, as a consequence of the methodological limitations and the well‐known pitfalls when analyzing natural language data, the NER results are likely to contain ambiguities. In this paper, we present an interactive NER ambiguity resolution technique, which enables users to create (post‐processing) rules for named entity recognition data based on the content and entity context of the analyzed documents. We specifically address the problem that in use‐cases where ambiguities are problematic, such as the attribution of fictional characters with traits, it is often unfeasible to train models on custom data to improve state of the art NER software. We derive an iterative process model for improving NER results, show an interactive NER ambiguity resolution prototype, illustrate our approach with contemporary literature, and discuss our work and future research.