Paper Info
Title
Interactive Ambiguity Resolution of Named Entities in Fictional Literature.
Abstract
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.
Year
DOI
Venue
2017
10.1111/cgf.13179
Comput. Graph. Forum
Field
DocType
Volume
Entity linking,Computer vision,Iterative and incremental development,Computer science,Software,Natural language,Natural language processing,Artificial intelligence,Ambiguity resolution,Named-entity recognition
Journal
36
Issue
ISSN
Citations 
3
0167-7055
1
PageRank 
References 
Authors
0.34
35
5
Name
Order
Citations
PageRank
Florian Stoffel11069.38
wolfgang jentner285.84
Michael Behrisch323417.70
johannes fuchs4173.45
Daniel A. Keim577041141.60