ishandutta0098 / entity-extraction

Entity Extraction using BERT

Entity Extraction using BERT

About the Dataset

Link to Dataset

Context

Annotated Corpus for Named Entity Recognition using GMB(Groningen Meaning Bank) corpus for entity classification with enhanced and popular features by Natural Language Processing applied to the data set.

Content

This is the extract from GMB corpus which is tagged, annotated and built specifically to train the classifier to predict named entities such as name, location, etc.

Number of tagged entities:

'O': 1146068', geo-nam': 58388, 'org-nam': 48034, 'per-nam': 23790, 'gpe-nam': 20680, 'tim-dat': 12786, 'tim-dow': 11404, 'per-tit': 9800, 'per-fam': 8152, 'tim-yoc': 5290, 'tim-moy': 4262, 'per-giv': 2413, 'tim-clo': 891, 'art-nam': 866, 'eve-nam': 602, 'nat-nam': 300, 'tim-nam': 146, 'eve-ord': 107, 'per-ini': 60, 'org-leg': 60, 'per-ord': 38, 'tim-dom': 10, 'per-mid': 1, 'art-add': 1

Essential info about entities:

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

Total Words Count = 1354149
Target Data Column: "tag"

References

• Building an entity extraction model using BERT
• HuggingFace Transformers Tokenizer
• HuggingFace Transformers Glossary