%0 Generic %1 lample2016neural %A Lample, Guillaume %A Ballesteros, Miguel %A Subramanian, Sandeep %A Kawakami, Kazuya %A Dyer, Chris %D 2016 %K final ner thema:sequence_labeling %T Neural Architectures for Named Entity Recognition %U http://arxiv.org/abs/1603.01360 %X 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. 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. 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. 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.

@misc{lample2016neural, 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. 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. 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. 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.}, added-at = {2018-11-25T17:09:00.000+0100}, author = {Lample, Guillaume and Ballesteros, Miguel and Subramanian, Sandeep and Kawakami, Kazuya and Dyer, Chris}, biburl = {https://www.bibsonomy.org/bibtex/214ea3585729ed1dde3d84435a444fed5/florianpircher}, description = {Neural Architectures for Named Entity Recognition}, interhash = {8f1980d565a9c5970e69e799d913378a}, intrahash = {14ea3585729ed1dde3d84435a444fed5}, keywords = {final ner thema:sequence_labeling}, note = {cite arxiv:1603.01360Comment: Proceedings of NAACL 2016}, timestamp = {2018-11-27T09:34:44.000+0100}, title = {Neural Architectures for Named Entity Recognition}, url = {http://arxiv.org/abs/1603.01360}, year = 2016 }