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.
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.