Comunicació presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.

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.

This work was sponsored in part by the Defense/nAdvanced Research Projects Agency (DARPA)/nInformation Innovation Office (I2O) under the/nLow Resource Languages for Emergent Incidents/n(LORELEI) program issued by DARPA/I2O under/nContract No. HR0011-15-C-0114. Miguel Ballesteros/nis supported by the European Commission under/nthe contract numbers FP7-ICT-610411 (project/nMULTISENSOR) and H2020-RIA-645012 (project/nKRISTINA).