Lightly-supervised Representation Learning with Global Interpretability

WS 2019 Marco A. Valenzuela-EscárcegaAjay NageshMihai Surdeanu

We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i.e., use of limited annotations and interpretability of extraction patterns, with the robust learning approaches proposed in representation learning. Our algorithm iteratively learns custom embeddings for both the multi-word entities to be extracted and the patterns that match them from a few example entities per category... (read more)

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