State-of-the-art named entity recognition systems/nrely heavily on hand-crafted features and/ndomain-specific knowledge in order to learn/neffectively from the small, supervised training/ncorpora that are available. In this paper, we/nintroduce two new neural architectures—one/nbased on bidirectional LSTMs and conditional/nrandom fields, and the other that constructs/nand labels segments using a transition-based/napproach inspired by shift-reduce parsers./nOur models rely on two sources of information/nabout ...
State-of-the-art named entity recognition systems/nrely heavily on hand-crafted features and/ndomain-specific knowledge in order to learn/neffectively from the small, supervised training/ncorpora that are available. In this paper, we/nintroduce two new neural architectures—one/nbased on bidirectional LSTMs and conditional/nrandom fields, and the other that constructs/nand labels segments using a transition-based/napproach inspired by shift-reduce parsers./nOur models rely on two sources of information/nabout words: character-based word/nrepresentations learned from the supervised/ncorpus and unsupervised word representations/nlearned from unannotated corpora. Our/nmodels obtain state-of-the-art performance in/nNER in four languages without resorting to/nany language-specific knowledge or resources/nsuch as gazetteers.
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