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Weakly supervised named entity classification

Edouard Grave 1, 2, 3, * 
* Corresponding author
2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique - ENS Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : 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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Submitted on : Monday, December 15, 2014 - 8:13:48 PM
Last modification on : Thursday, March 17, 2022 - 10:08:44 AM
Long-term archiving on: : Monday, March 16, 2015 - 12:46:29 PM


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  • 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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