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Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies

机译:通过嵌入结构依赖性改善生物医学类似实践检索

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Inferring the nature of the relationships between biomedical entities from text is an important problem due to the difficulty of maintaining human-curated knowledge bases in rapidly evolving fields. Neural word embed-dings have earned attention for an apparent ability to encode relational information. However, word embedding models that disregard syntax during training are limited in their ability to encode the structural relationships fundamental to cognitive theories of analogy. In this paper, we demonstrate the utility of encoding dependency structure in word embed-dings in a model we call Embedding of Structural Dependencies (ESD) as a way to represent biomedical relationships in two analogical retrieval tasks: a relationship retrieval (RR) task, and a literature-based discovery (LBD) task meant to hypothesize plausible relationships between pairs of entities unseen in training. We compare our model to skip-gram with negative sampling (SGNS), using 19 databases of biomedical relationships as our evaluation data, with improvements in performance on 17 (LBD) and 18 (RR) of these sets. These results suggest embeddings encoding dependency path information are of value for biomedical analogy retrieval.
机译:从推断生物医学文本实体之间的关系的性质是一个重要的问题,是由于在迅速发展的领域维持人体策划的知识库的难度。神经字嵌入-钟声已经赢得注意力的能力明显要编码的关系信息。然而,字嵌入模型训练中忽视语法的编码,以比喻的认知理论的基本结构关系的能力是有限的。在本文中,我们展示字编码依存结构的效用嵌入,钟声在我们称之为结构相关性(ESD)的嵌入,以此来表示两个模拟检索任务的生物医学的关系模型:一个关系检索(RR)的任务,和基于文献的发现(LBD)的任务意味着推测对在训练中看不见的实体之间的合理关系。我们比较我们的模型跳过克的负采样(SGNS),使用作为我们的评估数据生物医学的关系数据库19,这些套17(LBD)的性能提升和18度(RR)。这些结果表明编码的依赖路径信息的嵌入是生物医学类比检索值。

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