README.md

metadata

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
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
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

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