README.md

---
dataset_info:
  features:
  - name: input_ids
    sequence: int32
  - name: attention_mask
    sequence: int8
  - name: labels
    sequence: int64
  splits:
  - name: train
    num_bytes: 76440290.15811698
    num_examples: 120113
  - name: test
    num_bytes: 19110549.84188302
    num_examples: 30029
  download_size: 16997872
  dataset_size: 95550840
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
task_categories:
- token-classification
language:
- ko
size_categories:
- 100K<n<1M
---

## Dataset Summary
NER-News-BIDataset is a dataset for named entity recognition (NER) in news articles, publicly released by the National Institute of Korean Language in 2023.   
The dataset is labeled with named entities specifically for news data.   
It consists of a total of 150,142 sentences, and entities are categorized into 150 labels for recognition.  

## Languages
Korean  

## Data Structure
DatasetDict({    
    train: Dataset({    
        features: ['input_ids', 'attention_mask', 'labels'],   
        num_rows: 120113   
    })   
    test: Dataset({   
        features: ['input_ids', 'attention_mask', 'labels'],   
        num_rows: 30029   
    })   
})     

### Data Instances
The dataset is provided in text format with train/test sets.   
Each instance represents a news article, and if there is an entity in the sentence, it is appropriately tagged with the corresponding label.   
In cases where a single entity is separated into multiple tokens, the first token is labeled as "B-entity" and the subsequent tokens are labeled as "I-entity" until the end.  

### Data Fields
input_ids: "A processed named entity corpus of news articles constructed in 2022" has been tokenized and represented with numerical values.  
label: Identified a total of 151 entities, including the 0th label (not an entity). If counting both "B-entity" and "I-entity" labels for each entity, there are a total of 301 labels. 
The labeling is done with numerical values.  
The 151 types of labels are as follows:  

|index|0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32|33|34|35|36|37|38|39|40|41|42|43|44|45|46|47|48|49|50|51|52|53|54|55|56|57|58|59|60|61|62|63|64|65|66|67|68|69|70|71|72|73|74|75|76|77|78|79|80|81|82|83|84|85|86|87|88|89|90|91|92|93|94|95|96|97|98|99|100|101|102|103|104|105|106|107|108|109|110|111|112|113|114|115|116|117|118|119|120|121|122|123|124|125|126|127|128|129|130|131|132|133|134|135|136|137|138|139|140|141|142|143|144|145|146|147|148|149|150|151|152|153|154|155|156|157|158|159|160|161|162|163|164|165|166|167|168|169|170|171|172|173|174|175|176|177|178|179|180|181|182|183|184|185|186|187|188|189|190|191|192|193|194|195|196|197|198|199|200|201|202|203|204|205|206|207|208|209|210|211|212|213|214|215|216|217|218|219|220|221|222|223|224|225|226|227|228|229|230|231|232|233|234|235|236|237|238|239|240|241|242|243|244|245|246|247|248|249|250|251|252|253|254|255|256|257|258|259|260|261|262|263|264|265|266|267|268|269|270|271|272|273|274|275|276|277|278|279|280|281|282|283|284|285|286|287|288|289|290|291|292|293|294|295|296|297|298|299|300|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Label|O|B-PS\_NAME|B-PS\_CHARACTER|B-PS\_PET|B-FD\_SCIENCE|B-FD\_SOCIAL\_SCIENCE|B-FD\_MEDICINE|B-FD\_ART|B-FD\_HUMANITIES|B-FD\_OTHERS|B-TR\_SCIENCE|B-TR\_SOCIAL\_SCIENCE|B-TR\_MEDICINE|B-TR\_ART|B-TR\_HUMANITIES|B-TR\_OTHERS|B-AF\_BUILDING|B-AF\_CULTURAL\_ASSET|B-AF\_ROAD|B-AF\_TRANSPORT|B-AF\_MUSICAL\_INSTRUMENT|B-AF\_WEAPON|B-AFA\_DOCUMENT|B-AFA\_PERFORMANCE|B-AFA\_VIDEO|B-AFA\_ART\_CRAFT|B-AFA\_MUSIC|B-AFW\_SERVICE\_PRODUCTS|B-AFW\_OTHER\_PRODUCTS|B-OGG\_ECONOMY|B-OGG\_EDUCATION|B-OGG\_MILITARY|B-OGG\_MEDIA|B-OGG\_SPORTS|B-OGG\_ART|B-OGG\_MEDICINE|B-OGG\_RELIGION|B-OGG\_SCIENCE|B-OGG\_LIBRARY|B-OGG\_LAW|B-OGG\_POLITICS|B-OGG\_FOOD|B-OGG\_HOTEL|B-OGG\_OTHERS|B-LCP\_COUNTRY|B-LCP\_PROVINCE|B-LCP\_COUNTY|B-LCP\_CITY|B-LCP\_CAPITALCITY|B-LCG\_RIVER|B-LCG\_OCEAN|B-LCG\_BAY|B-LCG\_MOUNTAIN|B-LCG\_ISLAND|B-LCG\_CONTINENT|B-LC\_SPACE|B-LC\_OTHERS|B-CV\_CULTURE|B-CV\_TRIBE|B-CV\_LANGUAGE|B-CV\_POLICY|B-CV\_LAW|B-CV\_CURRENCY|B-CV\_TAX|B-CV\_FUNDS|B-CV\_ART|B-CV\_SPORTS|B-CV\_SPORTS\_POSITION|B-CV\_SPORTS\_INST|B-CV\_PRIZE|B-CV\_RELATION|B-CV\_OCCUPATION|B-CV\_POSITION|B-CV\_FOOD|B-CV\_DRINK|B-CV\_FOOD\_STYLE|B-CV\_CLOTHING|B-CV\_BUILDING\_TYPE|B-DT\_DURATION|B-DT\_DAY|B-DT\_WEEK|B-DT\_MONTH|B-DT\_YEAR|B-DT\_SEASON|B-DT\_GEOAGE|B-DT\_DYNASTY|B-DT\_OTHERS|B-TI\_DURATION|B-TI\_HOUR|B-TI\_MINUTE|B-TI\_SECOND|B-TI\_OTHERS|B-QT\_AGE|B-QT\_SIZE|B-QT\_LENGTH|B-QT\_COUNT|B-QT\_MAN\_COUNT|B-QT\_WEIGHT|B-QT\_PERCENTAGE|B-QT\_SPEED|B-QT\_TEMPERATURE|B-QT\_VOLUME|B-QT\_ORDER|B-QT\_PRICE|B-QT\_PHONE|B-QT\_SPORTS|B-QT\_CHANNEL|B-QT\_ALBUM|B-QT\_ADDRESS|B-QT\_OTHERS|B-EV\_ACTIVITY|B-EV\_WAR\_REVOLUTION|B-EV\_SPORTS|B-EV\_FESTIVAL|B-EV\_OTHERS|B-AM\_INSECT|B-AM\_BIRD|B-AM\_FISH|B-AM\_MAMMALIA|B-AM\_AMPHIBIA|B-AM\_REPTILIA|B-AM\_TYPE|B-AM\_PART|B-AM\_OTHERS|B-PT\_FRUIT|B-PT\_FLOWER|B-PT\_TREE|B-PT\_GRASS|B-PT\_TYPE|B-PT\_PART|B-PT\_OTHERS|B-MT\_ELEMENT|B-MT\_METAL|B-MT\_ROCK|B-MT\_CHEMICAL|B-TM\_COLOR|B-TM\_DIRECTION|B-TM\_CLIMATE|B-TM\_SHAPE|B-TM\_CELL\_TISSUE\_ORGAN|B-TMM\_DISEASE|B-TMM\_DRUG|B-TMI\_HW|B-TMI\_SW|B-TMI\_SITE|B-TMI\_EMAIL|B-TMI\_MODEL|B-TMI\_SERVICE|B-TMI\_PROJECT|B-TMIG\_GENRE|B-TM\_SPORTS|I-PS\_NAME|I-PS\_CHARACTER|I-PS\_PET|I-FD\_SCIENCE|I-FD\_SOCIAL\_SCIENCE|I-FD\_MEDICINE|I-FD\_ART|I-FD\_HUMANITIES|I-FD\_OTHERS|I-TR\_SCIENCE|I-TR\_SOCIAL\_SCIENCE|I-TR\_MEDICINE|I-TR\_ART|I-TR\_HUMANITIES|I-TR\_OTHERS|I-AF\_BUILDING|I-AF\_CULTURAL\_ASSET|I-AF\_ROAD|I-AF\_TRANSPORT|I-AF\_MUSICAL\_INSTRUMENT|I-AF\_WEAPON|I-AFA\_DOCUMENT|I-AFA\_PERFORMANCE|I-AFA\_VIDEO|I-AFA\_ART\_CRAFT|I-AFA\_MUSIC|I-AFW\_SERVICE\_PRODUCTS|I-AFW\_OTHER\_PRODUCTS|I-OGG\_ECONOMY|I-OGG\_EDUCATION|I-OGG\_MILITARY|I-OGG\_MEDIA|I-OGG\_SPORTS|I-OGG\_ART|I-OGG\_MEDICINE|I-OGG\_RELIGION|I-OGG\_SCIENCE|I-OGG\_LIBRARY|I-OGG\_LAW|I-OGG\_POLITICS|I-OGG\_FOOD|I-OGG\_HOTEL|I-OGG\_OTHERS|I-LCP\_COUNTRY|I-LCP\_PROVINCE|I-LCP\_COUNTY|I-LCP\_CITY|I-LCP\_CAPITALCITY|I-LCG\_RIVER|I-LCG\_OCEAN|I-LCG\_BAY|I-LCG\_MOUNTAIN|I-LCG\_ISLAND|I-LCG\_CONTINENT|I-LC\_SPACE|I-LC\_OTHERS|I-CV\_CULTURE|I-CV\_TRIBE|I-CV\_LANGUAGE|I-CV\_POLICY|I-CV\_LAW|I-CV\_CURRENCY|I-CV\_TAX|I-CV\_FUNDS|I-CV\_ART|I-CV\_SPORTS|I-CV\_SPORTS\_POSITION|I-CV\_SPORTS\_INST|I-CV\_PRIZE|I-CV\_RELATION|I-CV\_OCCUPATION|I-CV\_POSITION|I-CV\_FOOD|I-CV\_DRINK|I-CV\_FOOD\_STYLE|I-CV\_CLOTHING|I-CV\_BUILDING\_TYPE|I-DT\_DURATION|I-DT\_DAY|I-DT\_WEEK|I-DT\_MONTH|I-DT\_YEAR|I-DT\_SEASON|I-DT\_GEOAGE|I-DT\_DYNASTY|I-DT\_OTHERS|I-TI\_DURATION|I-TI\_HOUR|I-TI\_MINUTE|I-TI\_SECOND|I-TI\_OTHERS|I-QT\_AGE|I-QT\_SIZE|I-QT\_LENGTH|I-QT\_COUNT|I-QT\_MAN\_COUNT|I-QT\_WEIGHT|I-QT\_PERCENTAGE|I-QT\_SPEED|I-QT\_TEMPERATURE|I-QT\_VOLUME|I-QT\_ORDER|I-QT\_PRICE|I-QT\_PHONE|I-QT\_SPORTS|I-QT\_CHANNEL|I-QT\_ALBUM|I-QT\_ADDRESS|I-QT\_OTHERS|I-EV\_ACTIVITY|I-EV\_WAR\_REVOLUTION|I-EV\_SPORTS|I-EV\_FESTIVAL|I-EV\_OTHERS|I-AM\_INSECT|I-AM\_BIRD|I-AM\_FISH|I-AM\_MAMMALIA|I-AM\_AMPHIBIA|I-AM\_REPTILIA|I-AM\_TYPE|I-AM\_PART|I-AM\_OTHERS|I-PT\_FRUIT|I-PT\_FLOWER|I-PT\_TREE|I-PT\_GRASS|I-PT\_TYPE|I-PT\_PART|I-PT\_OTHERS|I-MT\_ELEMENT|I-MT\_METAL|I-MT\_ROCK|I-MT\_CHEMICAL|I-TM\_COLOR|I-TM\_DIRECTION|I-TM\_CLIMATE|I-TM\_SHAPE|I-TM\_CELL\_TISSUE\_ORGAN|I-TMM\_DISEASE|I-TMM\_DRUG|I-TMI\_HW|I-TMI\_SW|I-TMI\_SITE|I-TMI\_EMAIL|I-TMI\_MODEL|I-TMI\_SERVICE|I-TMI\_PROJECT|I-TMIG\_GENRE|I-TM\_SPORTS|
|Number|0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32|33|34|35|36|37|38|39|40|41|42|43|44|45|46|47|48|49|50|51|52|53|54|55|56|57|58|59|60|61|62|63|64|65|66|67|68|69|70|71|72|73|74|75|76|77|78|79|80|81|82|83|84|85|86|87|88|89|90|91|92|93|94|95|96|97|98|99|100|101|102|103|104|105|106|107|108|109|110|111|112|113|114|115|116|117|118|119|120|121|122|123|124|125|126|127|128|129|130|131|132|133|134|135|136|137|138|139|140|141|142|143|144|145|146|147|148|149|150|151|152|153|154|155|156|157|158|159|160|161|162|163|164|165|166|167|168|169|170|171|172|173|174|175|176|177|178|179|180|181|182|183|184|185|186|187|188|189|190|191|192|193|194|195|196|197|198|199|200|201|202|203|204|205|206|207|208|209|210|211|212|213|214|215|216|217|218|219|220|221|222|223|224|225|226|227|228|229|230|231|232|233|234|235|236|237|238|239|240|241|242|243|244|245|246|247|248|249|250|251|252|253|254|255|256|257|258|259|260|261|262|263|264|265|266|267|268|269|270|271|272|273|274|275|276|277|278|279|280|281|282|283|284|285|286|287|288|289|290|291|292|293|294|295|296|297|298|299|300|    

Frequency Statistics
|index|0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32|33|34|35|36|37|38|39|40|41|42|43|44|45|46|47|48|49|50|51|52|53|54|55|56|57|58|59|60|61|62|63|64|65|66|67|68|69|70|71|72|73|74|75|76|77|78|79|80|81|82|83|84|85|86|87|88|89|90|91|92|93|94|95|96|97|98|99|100|101|102|103|104|105|106|107|108|109|110|111|112|113|114|115|116|117|118|119|120|121|122|123|124|125|126|127|128|129|130|131|132|133|134|135|136|137|138|139|140|141|142|143|144|145|146|147|148|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Label|OGG\_POLITICS|CV\_POSITION|PS\_NAME|QT\_COUNT|LCP\_CITY|DT\_DAY|DT\_YEAR|LCP\_COUNTY|QT\_ORDER|DT\_OTHERS|TMM\_DISEASE|QT\_PRICE|QT\_MAN\_COUNT|DT\_DURATION|CV\_OCCUPATION|LC\_OTHERS|OGG\_ECONOMY|QT\_PERCENTAGE|OGG\_OTHERS|TMI\_PROJECT|LCP\_PROVINCE|AF\_TRANSPORT|OGG\_EDUCATION|LCP\_COUNTRY|EV\_OTHERS|AF\_BUILDING|CV\_LAW|TMI\_HW|OGG\_SPORTS|DT\_MONTH|CV\_RELATION|CV\_POLICY|CV\_FOOD|TI\_DURATION|TMI\_SERVICE|OGG\_MEDICINE|QT\_AGE|QT\_SIZE|AF\_ROAD|EV\_FESTIVAL|AM\_PART|EV\_SPORTS|CV\_PRIZE|TR\_SCIENCE|TM\_DIRECTION|OGG\_ART|QT\_OTHERS|PT\_GRASS|QT\_LENGTH|MT\_CHEMICAL|OGG\_SCIENCE|PT\_FRUIT|LCP\_CAPITALCITY|CV\_SPORTS|TMM\_DRUG|CV\_ART|LCG\_RIVER|AF\_CULTURAL\_ASSET|TM\_CELL\_TISSUE\_ORGAN|OGG\_RELIGION|QT\_SPORTS|QT\_WEIGHT|DT\_SEASON|AFA\_DOCUMENT|OGG\_MEDIA|TI\_OTHERS|TI\_HOUR|OGG\_MILITARY|LCG\_ISLAND|CV\_DRINK|LCG\_MOUNTAIN|CV\_TAX|CV\_FUNDS|TR\_MEDICINE|AFA\_VIDEO|AM\_MAMMALIA|OGG\_FOOD|MT\_ELEMENT|TM\_SPORTS|AM\_OTHERS|LCG\_CONTINENT|PT\_PART|OGG\_LAW|AFW\_OTHER\_PRODUCTS|CV\_CULTURE|AFW\_SERVICE\_PRODUCTS|CV\_CLOTHING|DT\_DYNASTY|FD\_MEDICINE|PT\_FLOWER|CV\_TRIBE|PT\_TREE|FD\_SCIENCE|TM\_COLOR|AM\_BIRD|QT\_ADDRESS|QT\_PHONE|CV\_LANGUAGE|TR\_SOCIAL\_SCIENCE|EV\_ACTIVITY|EV\_WAR\_REVOLUTION|CV\_SPORTS\_POSITION|OGG\_LIBRARY|AM\_TYPE|TMI\_SW|AFA\_MUSIC|DT\_WEEK|AFA\_PERFORMANCE|AFA\_ART\_CRAFT|FD\_HUMANITIES|QT\_VOLUME|TMI\_SITE|OGG\_HOTEL|LCG\_BAY|PS\_CHARACTER|LCG\_OCEAN|AM\_INSECT|AM\_FISH|QT\_TEMPERATURE|PT\_OTHERS|TM\_SHAPE|MT\_METAL|MT\_ROCK|AF\_MUSICAL\_INSTRUMENT|PT\_TYPE|QT\_SPEED|AF\_WEAPON|CV\_FOOD\_STYLE|LC\_SPACE|FD\_SOCIAL\_SCIENCE|CV\_SPORTS\_INST|TR\_ART|FD\_OTHERS|AM\_AMPHIBIA|AM\_REPTILIA|TMIG\_GENRE|TR\_OTHERS|TMI\_EMAIL|CV\_BUILDING\_TYPE|PS\_PET|TR\_HUMANITIES|DT\_GEOAGE|FD\_ART|CV\_CURRENCY|TMI\_MODEL|TI\_SECOND|QT\_CHANNEL|TM\_CLIMATE|TI\_MINUTE|
|Frequency|69683|43695|42060|30949|24791|19994|19836|19376|17908|17768|17622|15686|15460|15385|13634|13473|12744|12129|9912|9249|9084|8689|7475|7378|6144|5193|4875|4458|4440|4360|4002|3944|3537|3277|2993|2803|2659|2523|2465|2407|2401|2400|2231|2145|1999|1914|1911|1617|1615|1602|1589|1515|1395|1322|1307|1289|1258|1244|1165|1157|1145|1110|1097|987|980|979|976|967|937|884|869|859|857|855|837|775|752|720|715|689|688|683|667|631|583|505|467|453|445|441|437|410|395|391|391|388|383|370|367|367|362|337|304|296|285|283|275|273|265|245|240|229|222|220|220|204|192|191|188|158|151|149|148|130|126|124|113|110|107|82|52|43|42|41|40|37|35|34|30|25|22|19|19|11|8|8|5|3|2|    


### Data Splits
The dataset, consisting of 150,142 sentences, has been split in a ratio of 8:2. There are 120,113 sentences in the training set and 3,029 sentences in the test set.   

## Source Data
This dataset is based on the 'National Institute of Korean Language Named Entity Analysis Corpus 2022 (Version 1.1)' released by the National Institute of Korean Language in September 2023.   
For more detailed information, please refer to the National Institute of Korean Language website > Resources > Research Materials > '2022 Corpus Named Entity Analysis and Entity Linking' project report.   

### Citation
(국문) 국립국어원(2023). 국립국어원 개체명 분석 말뭉치 2022(버전 1.1) URL: https://corpus.korean.go.kr   
(Eng) National Institute of Korean Language(2023). NIKL Named Entity Corpus 2022 (v.1.1) URL: https://corpus.korean.go.kr