Papers › Context-Aware Representations for Knowledge Base Relation Extraction

Context-Aware Representations for Knowledge Base Relation Extraction

1 Sep 2017EMNLP 2017 9archive 2025-07-28

Daniil Sorokin, Iryna Gurevych

We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24 on a held-out set of relations. The code and the dataset to replicate the experiments are made available at \url{https://github.com/ukplab/}.

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