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Found 4 papers, 2 papers with code
Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction
The goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form {\textless}subject, predicate, object{\textgreater}.
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction
Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM).
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains
In this paper, we propose to improve on this process by pre-training an entity encoder such that embeddings of coreferring entities are more similar to each other than to the embeddings of other entities.
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence
To alleviate the issue, we propose a novel intermediate training task, names meaning-matching, designed to directly learn a meaning-text correspondence, instead of relying on the distributional hypothesis.
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