[Lindrian]

Don't agree. LSTM is a very good option for NER. I have personally experimented with simple FFNN and language models to accommodate variable width input and achieved very good results. I will be publishing a paper on this soon.

The system in the article fails at quite simple sentences, such as:

"Rex Tillerson is the secretary of state."

It tags "Rex Tillerson" as ORG instead of PER. If you change "is" to "was" it tags it as PER.

[melzarei]

Disclaimer: as you may notice, the tagger is far from being perfect. Some errors are due to the fact that the demo uses a reduced vocabulary (lighter for the API). But not all. It is also very sensible to capital letters, which comes both from the architecture of the model and the training data. For more information about the demo, see here.