I was watching Manning's lecture on evaluation of NER models, and I'm confused why between 4:07 and 5:13, he states that the error in not labeling one word in a sequence while correctly labeling the rest is considered as both a false positive (fp) and a false negative (fn). Based on my understanding, a fp is when you label a word as an entity when it is not actually that entity and a fn is when you don't label a word as an entity when it is truly is an entity. For those unable to watch the video, the example was:
Sentence: First Bank of Chicago
True label: ORG ORG ORG ORG
Model: O ORG ORG ORG
- In the example, the model's prediction fails to label an entity that is truly an entity. Why is this not considered only a fp?
- Manning states that it is better not to have labeled any of the words as entities rather than naming part of the sequence correctly. Why is this true?