Universal schema (USchema) assumes that two sentence patterns that share the same entity pairs are similar to each other. This assumption is widely adopted for solving various types of relation extraction (RE) tasks. Nevertheless, each sentence pattern could contain multiple facets, and not every facet is similar to all the facets of another sentence pattern co-occurring with the same entity pair. To address the violation of the USchema assumption, we propose multi-facet universal schema that uses a neural model to represent each sentence pattern as multiple facet embeddings and encourage one of these facet embeddings to be close to that of another sentence pattern if they co-occur with the same entity pair. In our experiments, we demonstrate that multi-facet embeddings significantly outperform their single-facet embedding counterpart, compositional universal schema (CUSchema) (Verga et al., 2016), in distantly supervised relation extraction tasks. Moreover, we can also use multiple embeddings to detect the entailment relation between two sentence patterns when no manual label is available.
翻译:宇宙化学(USchema) 假设, 共有相同实体对对的两种句式模式是相似的。 这一假设被广泛采用, 用于解决各种类型的关系提取( RE) 任务。 然而, 每个句式模式可能包含多个方面, 而不是每个面体都类似于与同一实体对口同时出现的另一个句式模式的所有方面。 为了解决违反 Uschema 假设的问题, 我们提出了多面通用模式, 使用一个神经模式来代表每个句式模式作为多重面嵌入, 并鼓励其中的一个面型嵌入在与同一个实体对口同时发生时接近另一句式模式。 在我们的实验中, 我们证明多面嵌入大大超越了单面嵌入式对口的单面嵌入式模式, 构成式通用色片( CUSchema)( Verga等人, 2016年), 在远处监督的提取关系任务中。 此外, 在没有手工标签的情况下, 我们还可以使用多个嵌入来检测两种句式模式之间的关联。