BERTGraph, which adds rule-extracted relational graphs to BERT via a graph convolutional network, improves news classification accuracy over fine-tuned BERT on N24News and Snopes datasets.
Neural Open Information Extraction
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abstract
Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.
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Knowledge Bases in Support of Large Language Models for Processing Web News
BERTGraph, which adds rule-extracted relational graphs to BERT via a graph convolutional network, improves news classification accuracy over fine-tuned BERT on N24News and Snopes datasets.