Named Entity Recognition
Named Entity Recognition refers to an interesting information extraction technique in the area of machine learning, with the help of which certain types of entities can be identified using annotations. This plays a major role in giving solutions to real world queries such as whether a tweet mentions a particular person's name or location, to find the sentiments about a particular product etc. Figure 1 shows the various Named Entity Recognition and Information Extraction (IE) techniques (Christopher, Prabhakar, & Hinrich, 2008).
The efficiency of the NER approach can be evaluated using the following measures (Powers, 2011). TP, FP and FN refer to “True positives”, “False positives” and “False negatives,” respectively.
Precision (P): This refers to the ratio of correctly predicted entities to all the entity predictions.
(1)Recall(R): This refers to the ratio of correctly predicted entities to all the real entities.
(2)F-Measure(F): This is the harmonic mean of both precision and recall; which helps to average ratios in a suitable manner.
(3)