Named Entity Recognition (NER)

Named Entity Recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.

Data Set used - https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus

Tag Distribution

The GMB dataset utilizes IOB tagging or Inside, Outside Beginning. IOB is a common tagging format for tagging tokens which we have discussed earlier. To refresh your memory:

• I- prefix before a tag indicates that the tag is inside a chunk.
• B- prefix before a tag indicates that the tag is the beginning of a chunk.
• O- tag indicates that a token belongs to no chunk (outside).

The tags in this dataset are explained as follows:

• geo = Geographical Entity
• org = Organization
• per = Person
• gpe = Geopolitical Entity
• tim = Time indicator
• art = Artifact
• eve = Event
• nat = Natural Phenomenon

Anything outside these classes is termed as other, denoted as O.

1- Conditional Random Fields

CRF is an undirected graphical model whose nodes can be divided into exactly two disjoint sets $X$ and $Y$, the observed and output variables, respectively; the conditional distribution $p(Y|X)$ is then modeled.

2- Spacy

3- Transforemers in [Transformers Folder ] (https://github.com/ShreeCharranR/Transformers---NLP-Experiments)