1. Conclusion

This paper presents two neural architectures for sequence labeling that provide the best NER results ever reported in standard evaluation settings, even compared with models that use external resources, such as gazetteers. A key aspect of our models are that they model output label dependencies, either via a simple CRF architecture, or using a transition-based algorithm to explicitly construct and label chunks of the input. Word representations are also crucially important for success: we use both pre-trained word representations and “character-based” representations that capture morphological and orthographic information. To prevent the learner from depending too heavily on one representation class, dropout is used.

2. Acknowledgments

This work was sponsored in part by the Defense Advanced Research Projects Agency (DARPA) Information Innovation Office (I2O) under the Low Resource Languages for Emergent Incidents (LORELEI) program issued by DARPA/I2O under Contract No. HR0011-15-C-0114. Miguel Ballesteros is supported by the European Commission under the contract numbers FP7-ICT-610411 (project MULTISENSOR) and H2020-RIA-645012 (project KRISTINA).