Context-Aware Representations for Knowledge Base Relation Extraction
Daniil Sorokin, Iryna Gurevych · 2017
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 https://github.com/ukplab.