CANDiS: Coupled & Attention-Driven Neural Distant Supervision

Distant Supervision for Relation Extraction uses heuristically aligned text data with an existing knowledge base as training data. The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data. Previous work has expl...

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Bibliographic Details
Published in:arXiv.org 2017-10
Main Authors: Nagarajan, Tushar, Sharmistha, Talukdar, Partha
Format: Article
Language:English
Subjects:
Couplings
Knowledge base
Neural networks
Noise reduction
Supervision
Training
Online Access:Get full text
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Summary:Distant Supervision for Relation Extraction uses heuristically aligned text data with an existing knowledge base as training data. The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data. Previous work has explored relationships among instances of the same entity-pair to reduce this noise, but relationships among instances across entity-pairs have not been fully exploited. We explore the use of inter-instance couplings based on verb-phrase and entity type similarities. We propose a novel technique, CANDiS, which casts distant supervision using inter-instance coupling into an end-to-end neural network model. CANDiS incorporates an attention module at the instance-level to model the multi-instance nature of this problem. CANDiS outperforms existing state-of-the-art techniques on a standard benchmark dataset.
ISSN:2331-8422