A study of coherence in entity linking

Entity Linking, Machine Learning, NLP

Duncan, Chase ; Roth ; Dan

关键词: Entity Linking, Machine Learning, NLP;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/102393/DUNCAN-THESIS-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Entity linking (EL) is the task of mapping entities, such as persons, locations, organizations, etc., in text to a corresponding record in a knowledge base (KB) like Wikipedia or Freebase. In this paper we present, for the first time, a controlled study of one aspect of this problem called coherence. Further we show that many state-of-the-art models for EL reduce to the same basic architecture. Based on this general model we suggest that any system can theoretically bene t from using coherence although most do not. Our experimentation suggests that this is because the common approaches to measuring coherence among entities produce only weak signals. Therefore we argue that the way forward for research into coherence in EL is not by seeking new methods for performing inference but rather better methods for representing and comparing entities based off of existing structured data resources such as DBPedia and Wikidata.