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Conference Papers Year : 2014

Weakly supervised named entity classification


In this paper, we describe a new method for the problem of named entity classifica-tion for specialized or technical domains, using distant supervision. Our approach relies on a simple observation: in some specialized domains, named entities are almost unambiguous. Thus, given a seed list of names of entities, it is cheap and easy to obtain positive examples from unlabeled texts using a simple string match. Those positive examples can then be used to train a named entity classifier, by using the PU learning paradigm, which is learning from positive and unlabeled examples. We introduce a new convex formulation to solve this problem, and apply our technique in order to extract named entities from financial reports cor-responding to healthcare companies.
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Dates and versions

hal-01095596 , version 1 (15-12-2014)




  • HAL Id : hal-01095596 , version 1


Edouard Grave. Weakly supervised named entity classification. Workshop on Automated Knowledge Base Construction (AKBC), Dec 2014, Montréal, Canada. ⟨hal-01095596⟩
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