metadata

annotations_creators:
  - Abhinav Walia (Owner)
language:
  - en
license: 'Database: Open Database, Contents: Database Contents'

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

from Kaggle Datasets

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.