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首页> 外文会议>3rd workshop on representation learning for NLP 2018 >Jointly Embedding Entities and Text with Distant Supervision
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Jointly Embedding Entities and Text with Distant Supervision

机译：通过远距离监督将实体和文本联合嵌入

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摘要

Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.
机译：对于NLP应用程序，学习知识实体和概念的表示形式变得越来越重要。但是，最近的实体嵌入方法依赖于结构化资源，而这些资源对于新域和语料库而言创建起来很昂贵。我们提出了一种远程监督的方法，用于仅使用实体与表面形式之间的映射列表，即可从未标注的语料库中共同学习实体和文本的嵌入。我们从开放域和生物医学语料库中学习嵌入，并与依赖于人类注释文本或大型知识图结构的现有方法进行比较。在现有的生物医学数据集和我们发布给社区的新的基于维基百科的数据集中，我们的嵌入都比以前的工作更好地捕获了实体的相似性和相关性。类比完成和实体意义消除歧义的结果表明，实体和单词捕获了可以被有效组合以供下游使用的补充信息。

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  • 会议地点 Melbourne(AU)
  • 作者

    Denis Newman-Griffis; Albert M Lai; Eric Fosler-Lussier;

  • 作者单位

    Department of Computer Science and Engineering, The Ohio State University, Columbus, OH,Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD;

    Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD,Institute for Informatics, Washington University in St. Louis, St. Louis, MO;

    Department of Computer Science and Engineering, The Ohio State University, Columbus, OH;

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