Many NLP systems make use of NER systems:
- Search engine efficiency
- Recommendation engine
- Customer service (assigning proper agent)
- Automatic trading

University of Michigan - Ann Arbor

Named entity recognition is to find and classify spans of text that correspond to names of people, places or organizations. To use sequence labeling for a span-recognition problem, we’ll use a technique called IOB encoding.

Sequence labeling: named entity recognition

Natural Language Processing with Sequence Models is the third course in the Natural Language Prcessing Specialization published by deeplearning.ai.

Coursera: Natural Language Processing with Sequence Models

For one class of entity, we label any token that begins a span of named entity with the label B, tokens that occur inside a span are tagged with an I, and any tokens outside of any span of interest are labeled O.

IOB encoding (single entity)

- Choosing the maximum probability label for each element in a sequence independently does not necessarily result in an optimal (or even very good) sequence of tags.
- It doesn't guarantee the resulting sequence is well-formed or legal. For example, tag O might be followed by tag I; a tag I-LOC following a tag B-PER.

Drawbacks of IOB tagging

Applications of Named Entity Recognition (NER) 

The standard IOB (Inside, Outside, Beginning) tagging scheme uses the tag set $${\mathrm{B}, \mathrm{I}, \mathrm{O}}$$ to identify entities. When extending this to multiple entity types, the 'B' and 'I' tags are specialized for each class. For example, $$\mathrm{B}\textrm{-}\mathrm{ORG}$$ signifies the beginning of an organization's name, $$\mathrm{I}\textrm{-}\mathrm{ORG}$$ indicates a word inside an organization, and $$\mathrm{O}$$ means the word does not belong to any named entity. This specialization expands the total tag set size to $${}2 \times N + 1$$, where $$N$$ is the number of distinct entity classes.

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