True Negatives for Named Entity Recognition Are True Negatives always zero for a Named Entity Recognition task because TN in NER would mean a not entity being classified as Not entity?
Actual Entity
[Microsoft Corp.] CEO [Steve Ballmer] announced the release of [Windows7] today
Model prediction
[Microsoft Corp.] [CEO] [Steve] Ballmer announced the release of Windows7 [today]
What will be the TNs in the above example?
By definition, true negative is non-entity tokens in the text, agreed by gold standard. So;
TN: 5 = {announced, the, release, of}
But, in practice the number of true negative tokens are quite high and this heavily dominates the metrics, and any measure using TN (like accuracy) becomes highly controversial. Instead, preferred better metrics:

Recall: TP/(TP+FN) - coverage in all correct entities, and
Precision: TP/(TP+FP) - how reliable for a positive result
F1 Score (harmonic mean of precision and recall) can be used as a balanced metrics.