1. Abstract

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.

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state-of-the art：最先进的
hand-crafted features：手动设计的特征
corpora：语料库

In this paper, we introduce two new neural architectures—one based on bidirectional LSTMs and conditional random fields, and the other that constructs and labels segments using a transition-based approach inspired by shift-reduce parsers.

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传统方法：基于人肉选feature和领域知识
新方法：
（1）双向LSTM + CRF
（2）移位归约分析 + 基于转移的方法

Our models rely on two sources of information about words: character-based word representations learned from the supervised corpus and unsupervised word representations learned from unannotated corpora.

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使用两种表示方法：
（1）基于标注语料库的字符级单词表示
（2）基于未标注语料库的单词级表示

Our models obtain state-of-the-art performance in NER in four languages without resorting to any language-specific knowledge or resources such as gazetteers.

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优点：
（1）性能好
（2）不需要特定语言的相关知识
（3）不需要gazetteers资源