What is the Name Entity Recognition (NER) Dataset?

The Name Entity Recognition (NER) Dataset is an annotated text dataset based on the GMB corpus. It provides word-level labels for identifying different types of named entities in text.

How many sentences are included in the NER Dataset?

The dataset contains approximately 47,959 sentences and around 1.35 million words.

What columns are included in the NER Dataset?

The dataset includes Sentence #, Word, POS, and Tag columns. These fields identify sentences, individual words, part-of-speech information, and named entity labels.

What entity types are included in the NER Dataset?

The dataset includes categories such as geographical entities, organizations, persons, geopolitical entities, time indicators, artifacts, events, and natural phenomena.

What can the NER Dataset be used for?

The dataset can support named entity recognition, information extraction, natural language processing, sequence labeling, text analysis, machine learning, and deep learning projects.

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