


**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. 
 
## Modifications
 
the ner_dataset.csv was modified to have a similar data Structure as [CoNLL-2003 dataset](https://huggingface.co/datasets/conll2003) 

## Licensing information

Database: Open Database, Contents: Database Contents.
    
annotations_creators:Abhinav Walia (Owner) language:en license:odbl region:us
{"annotations_creators": ["Abhinav Walia (Owner)"], "language": ["en"], "license": ["odbl"]}
TAGS
#annotations_creators-Abhinav Walia (Owner) #language-English #license-odbl #region-us 




Date: 2022-07-10<br/>
Files: ner_dataset.csv<br/>
Source: Kaggle entity annotated corpus<br/>
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. 
 
## 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.
    
# 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. ## 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.
TAGS
#annotations_creators-Abhinav Walia (Owner) #language-English #license-odbl #region-us 
 # 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. ## 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.
passage: TAGS
#annotations_creators-Abhinav Walia (Owner) #language-English #license-odbl #region-us 
# 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.