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--- |
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dataset_info: |
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features: |
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- name: input_ids |
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sequence: int32 |
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- name: attention_mask |
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sequence: int8 |
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- name: labels |
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sequence: int64 |
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splits: |
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- name: train |
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num_bytes: 76440290.15811698 |
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num_examples: 120113 |
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- name: test |
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num_bytes: 19110549.84188302 |
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num_examples: 30029 |
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download_size: 16997872 |
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dataset_size: 95550840 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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task_categories: |
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- token-classification |
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language: |
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- ko |
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size_categories: |
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- 100K<n<1M |
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--- |
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## dataset summary |
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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. |
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The dataset is labeled with named entities specifically for news data. |
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It consists of a total of 150,142 sentences, and entities are categorized into 150 labels for recognition. |
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## Languages |
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Korean |
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## Data Structure |
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DatasetDict({ |
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train: Dataset({ |
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features: ['input_ids', 'attention_mask', 'labels'], |
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num_rows: 120113 |
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}) |
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test: Dataset({ |
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features: ['input_ids', 'attention_mask', 'labels'], |
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num_rows: 30029 |
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}) |
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}) |
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### Data Instances |
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The dataset is provided in text format with train/test sets. |
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Each instance represents a news article, and if there is an entity in the sentence, it is appropriately tagged with the corresponding label. |
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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. |
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### Data Fields |
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input_ids: "A processed named entity corpus of news articles constructed in 2022" has been tokenized and represented with numerical values. |
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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. |
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The labeling is done with numerical values. |
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The 151 types of labels are as follows: |
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|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| |
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| |
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|Label|O|IGN|PS\_NAME|PS\_CHARACTER|PS\_PET|FD\_SCIENCE|FD\_SOCIAL\_SCIENCE|FD\_MEDICINE|FD\_ART|FD\_HUMANITIES|FD\_OTHERS|TR\_SCIENCE|TR\_SOCIAL\_SCIENCE|TR\_MEDICINE|TR\_ART|TR\_HUMANITIES|TR\_OTHERS|AF\_BUILDING|AF\_CULTURAL\_ASSET|AF\_ROAD|AF\_TRANSPORT|AF\_MUSICAL\_INSTRUMENT|AF\_WEAPON|AFA\_DOCUMENT|AFA\_PERFORMANCE|AFA\_VIDEO|AFA\_ART\_CRAFT|AFA\_MUSIC|AFW\_SERVICE\_PRODUCTS|AFW\_OTHER\_PRODUCTS|OGG\_ECONOMY|OGG\_EDUCATION|OGG\_MILITARY|OGG\_MEDIA|OGG\_SPORTS|OGG\_ART|OGG\_MEDICINE|OGG\_RELIGION|OGG\_SCIENCE|OGG\_LIBRARY|OGG\_LAW|OGG\_POLITICS|OGG\_FOOD|OGG\_HOTEL|OGG\_OTHERS|LCP\_COUNTRY|LCP\_PROVINCE|LCP\_COUNTY|LCP\_CITY|LCP\_CAPITALCITY|LCG\_RIVER|LCG\_OCEAN|LCG\_BAY|LCG\_MOUNTAIN|LCG\_ISLAND|LCG\_CONTINENT|LC\_SPACE|LC\_OTHERS|CV\_CULTURE|CV\_TRIBE|CV\_LANGUAGE|CV\_POLICY|CV\_LAW|CV\_CURRENCY|CV\_TAX|CV\_FUNDS|CV\_ART|CV\_SPORTS|CV\_SPORTS\_POSITION|CV\_SPORTS\_INST|CV\_PRIZE|CV\_RELATION|CV\_OCCUPATION|CV\_POSITION|CV\_FOOD|CV\_DRINK|CV\_FOOD\_STYLE|CV\_CLOTHING|CV\_BUILDING\_TYPE|DT\_DURATION|DT\_DAY|DT\_WEEK|DT\_MONTH|DT\_YEAR|DT\_SEASON|DT\_GEOAGE|DT\_DYNASTY|DT\_OTHERS|TI\_DURATION|TI\_HOUR|TI\_MINUTE|TI\_SECOND|TI\_OTHERS|QT\_AGE|QT\_SIZE|QT\_LENGTH|QT\_COUNT|QT\_MAN\_COUNT|QT\_WEIGHT|QT\_PERCENTAGE|QT\_SPEED|QT\_TEMPERATURE|QT\_VOLUME|QT\_ORDER|QT\_PRICE|QT\_PHONE|QT\_SPORTS|QT\_CHANNEL|QT\_ALBUM|QT\_ADDRESS|QT\_OTHERS|EV\_ACTIVITY|EV\_WAR\_REVOLUTION|EV\_SPORTS|EV\_FESTIVAL|EV\_OTHERS|AM\_INSECT|AM\_BIRD|AM\_FISH|AM\_MAMMALIA|AM\_AMPHIBIA|AM\_REPTILIA|AM\_TYPE|AM\_PART|AM\_OTHERS|PT\_FRUIT|PT\_FLOWER|PT\_TREE|PT\_GRASS|PT\_TYPE|PT\_PART|PT\_OTHERS|MT\_ELEMENT|MT\_METAL|MT\_ROCK|MT\_CHEMICAL|TM\_COLOR|TM\_DIRECTION|TM\_CLIMATE|TM\_SHAPE|TM\_CELL\_TISSUE\_ORGAN|TMM\_DISEASE|TMM\_DRUG|TMI\_HW|TMI\_SW|TMI\_SITE|TMI\_EMAIL|TMI\_MODEL|TMI\_SERVICE|TMI\_PROJECT|TMIG\_GENRE|TM\_SPORTS| |
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|Number|0|-100|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|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| |
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### Data Splits |
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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. |
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## Source Data |
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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. |
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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. |
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### Citation |
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(국문) 국립국어원(2023). 국립국어원 개체명 분석 말뭉치 2022(버전 1.1) URL: https://corpus.korean.go.kr |
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(Eng) National Institute of Korean Language(2023). NIKL Named Entity Corpus 2022 (v.1.1) URL: https://corpus.korean.go.kr |
