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.