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	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