ProtagonistTagger -- a Tool for Entity Linkage of Persons in Texts from Various Languages and Domains
This tool addresses entity linkage for persons in multilingual and multi-domain texts, but it appears incremental as it builds on existing NER/NED methods.
The authors tackled the problem of person named entity recognition and disambiguation in texts from diverse languages and domains, achieving performance between 78% and 88% in precision and recall.
Named entities recognition (NER) and disambiguation (NED) can add semantic context to the recognized named entities in texts. Named entity linkage in texts, regardless of a domain, provides links between the entities mentioned in unstructured texts and individual instances of real-world objects. In this poster, we present a tool - protagonistTagger - for person NER and NED in texts. The tool was tested on texts extracted from classic English novels and Polish Internet news. The tool's performance (both precision and recall) fluctuates between 78% and even 88%.