Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction

Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction

Various tasks in natural language processing (NLP) suffer from lack of labelled training data, which deep neural networks are hungry for. In this paper, we relied upon features learned to generate relation triples from the open information extraction (OIE) task. First, we studied how transferable th...

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Bibliographic Details
Main Authors: Injy Sarhan, Marco Spruit
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Applied Sciences
Subjects:
transfer learning
open information extraction
relation extraction
recurrent neural networks
word embeddings
Online Access:https://www.mdpi.com/2076-3417/10/17/5758

Internet

https://www.mdpi.com/2076-3417/10/17/5758

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