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---
Annotator:
- Abhinav Walia (Owner)
language:
- en
tags:
- translation
license:
- Database Open Database
- Contents Database Contents
---
**Date**: 2022-07-10<br/>
**Files**: ner_dataset.csv<br/>
**Source**: [Kaggle entity annotated corpus](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus)<br/>
**notes**: The dataset only contains the tokens and ner tag labels. Labels are uppercase.
# About Dataset
[**from Kaggle Datasets**](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus)
## 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.
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