Named-entity recognition: SVM or RNN? | AI

For some time I work on MindForger, which is a knowledge management tool. Users work with (personal) remarks, research papers/logs, how-tos, documentations etc. My goal is to make it "smarter" than usual apps in this domain.

I use Support Vector Machine (SVM) to perform named-entity recognition (via MITIE/dlib). However, a default model for recognition of person/location/organization names is relatively big (~350MB) and it loads ~10s. Recognition itself is fast, but it has relatively high number of false positives.

I'm looking for an advice based on real use of NER: Should I go with SVMs (tune model, eliminate false positives using pre/post processing, etc.) or should I rather use RNNs (recurrent neural networks @ LSTM/GRU/…)?

I have a number of use cases for NER in MindForger. In case of common entities (person/organization/… names) big data sets are available, however, in case of other entity types I may have as less as <10k examples.

What's your NER suggestion for a desktop application? I look forward a method which has better results in general (and it's worth to invest time in tuning its model) and is reasonably fast (initialization and recognition).