IOB2

IOB2, also known as the Inside-Out Backward Object, is a data encoding scheme primarily used in Natural Language Processing (NLP) for tasks like Named Entity Recognition (NER). It builds upon the simpler IOB (Inside-Outside-Beginning) tagging format. In IOB2, each token in a sentence is assigned a tag that indicates whether it is part of a named entity and its position within that entity. The scheme uses three types of tags: B (Beginning), I (Inside), and O (Outside).

The 'B' tag signifies the first token of a named entity. For example, in the sentence "Barack Obama was born in Hawaii," "Barack" would be tagged as B-PERSON. The 'I' tag denotes a token that is inside a named entity but not the first token. Following the previous example, "Obama" would be tagged as I-PERSON. The 'O' tag is used for tokens that are not part of any named entity, such as "was," "born," "in," and "Hawaii" (if "Hawaii" were not considered a location entity in this context).

A key advantage of IOB2 over the original IOB format is its improved ability to handle consecutive entities of the same type. In IOB, a sequence like "New York City" tagged as B-LOCATION I-LOCATION I-LOCATION could be ambiguous if another location immediately followed. IOB2 resolves this by ensuring that any token starting a new entity, even if it's the same type as the previous one, receives a 'B' tag. For instance, if "New York" and "New Jersey" were both locations, they would be tagged B-LOCATION I-LOCATION and B-LOCATION I-LOCATION respectively. This stricter definition aids in the disambiguation and accurate extraction of entities by machine learning models.