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--- |
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Annotator: |
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- Abhinav Walia (Owner) |
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language: |
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- en |
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tags: |
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- translation |
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license: |
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- Database Open Database |
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- Contents Database Contents |
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--- |
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**Date**: 2022-07-10<br/> |
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**Files**: ner_dataset.csv<br/> |
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**Source**: [Kaggle entity annotated corpus](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus)<br/> |
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**notes**: The dataset only contains the tokens and ner tag labels. Labels are uppercase. |
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# About Dataset |
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[**from Kaggle Datasets**](https://www.kaggle.com/datasets/abhinavwalia95/entity-annotated-corpus) |
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## Context: |
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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. |
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Tip: Use Pandas Dataframe to load dataset if using Python for convenience. |
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## Content: |
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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. |
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Number of tagged entities: |
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'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 |
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## Essential info about entities: |
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* geo = Geographical Entity |
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* org = Organization |
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* per = Person |
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* gpe = Geopolitical Entity |
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* tim = Time indicator |
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* art = Artifact |
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* eve = Event |
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* nat = Natural Phenomenon |
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* Total Words Count = 1354149 |
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* Target Data Column: "tag" (ner_tag in this repo) |
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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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