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End of preview. Expand
in Dataset Viewer.
Date: 2022-07-10
Files: ner_dataset.csv
Source: Kaggle entity annotated corpus
notes: The dataset only contains the tokens and ner tag labels. Labels are uppercase.
About 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.
Tip: Use Pandas Dataframe to load dataset if using Python for convenience.
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" (ner_tag in this repo)
Inspiration: This dataset is getting more interested because of more features added to the recent version of this dataset. Also, it helps to create a broad view of Feature Engineering with respect to this dataset.
Modifications
the ner_dataset.csv was modified to have a similar data Structure as CoNLL-2003 dataset
Licensing information
Database: Open Database, Contents: Database Contents.
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