This results in the model picking out spurious words alongside the actual entity, which necessitates the use of stop-word and symbol removal.
we observe that our proposed approach significantly outperforms the baseline, and performs reasonably well when compared to various stateof-the-art supervised approaches that use significantly more ground-truth annotation information.