论文标题
名称标签的全球关注
Global Attention for Name Tagging
论文作者
论文摘要
许多名称标记方法使用局部上下文信息取得了很大成功，但是当本地上下文模棱两可或有限时失败。我们提出了一个新框架，以利用本地，文档级和语料库级上下文信息来改善名称标签。我们从同一文档中的其他句子和语料库级上下文中从其他局部相关文档中的句子中检索文档级别的上下文。我们提出了一个模型，该模型将通过全球关注来将文档级别和语料库级的上下文信息以及本地上下文信息融合在一起，该信息通过全球关注，该信息动态加权其各自的上下文信息和门控机制，从而确定此信息的影响。基准数据集的广泛实验显示了我们方法的有效性，该方法在CONLL-2002和CONLL-2003数据集中为荷兰，德语和西班牙语实现了最先进的结果。
Many name tagging approaches use local contextual information with much success, but fail when the local context is ambiguous or limited. We present a new framework to improve name tagging by utilizing local, document-level, and corpus-level contextual information. We retrieve document-level context from other sentences within the same document and corpus-level context from sentences in other topically related documents. We propose a model that learns to incorporate document-level and corpus-level contextual information alongside local contextual information via global attentions, which dynamically weight their respective contextual information, and gating mechanisms, which determine the influence of this information. Extensive experiments on benchmark datasets show the effectiveness of our approach, which achieves state-of-the-art results for Dutch, German, and Spanish on the CoNLL-2002 and CoNLL-2003 datasets.
