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Datasets:
yeajinmin
/
NER-News-BIDataset

Tasks:
Token Classification
Languages:
Korean
Size Categories:
100K<n<1M
Tags:
Dataset card Viewer Files Community
4
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sequence
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[ 0, 30297, 146466, 12823, 86410, 697, 52, 371, 8443, 19123, 12284, 769, 6, 60600, 203379, 1291, 11105, 218655, 4, 6521, 8825, 4403, 5050, 1291, 6521, 21839, 5050, 4, 14592, 3614, 67028, 43184, 3999, 26, 4877, 8280, 127266, 697, 182824, 688, 63054, 1963, 7592, 23717, 6, 91921, 17421, 5191, 59467, 469, 5645, 49067, 6, 189705, 1077, 9170, 12284, 2020, 11239, 6521, 12284, 1083, 15996, 31475, 95389, 6521, 40629, 5837, 9452, 143026, 144455, 6, 115113, 1077, 3162, 63, 9354, 4, 52, 371, 5931, 13420, 9045, 18683, 237682, 4, 6521, 21314, 7094, 71623, 4, 107063, 175727, 134796, 26, 4877, 20110, 1571, 112932, 769, 48932, 4, 204, 1875, 20089, 3761, 6, 135931, 203162, 73250, 1291, 6, 80624, 6720, 4, 69675, 199636, 183460, 6720, 26, 4877, 130928, 24877, 367, 73250, 47869, 688, 11270, 54122, 1963, 249981, 1875, 63, 1077, 2625, 37057, 367, 6, 69187, 480, 21163, 8000, 31890, 5476, 5, 2 ]
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
[ 0, 0, 1, 151, 72, 0, 0, 0, 46, 196, 196, 0, 0, 0, 0, 0, 0, 0, 0, 18, 168, 168, 168, 0, 18, 168, 168, 0, 40, 190, 190, 190, 190, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 196, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 180, 180, 180, 180, 0, 30, 180, 180, 180, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ 0, 215035, 160944, 160363, 140248, 41786, 993, 204, 7849, 7849, 141696, 106, 154432, 118087, 6, 202651, 152981, 78515, 6292, 3761, 37619, 203718, 105801, 6547, 16, 26, 480, 81245, 184711, 2280, 125401, 7956, 469, 152981, 116183, 413, 7063, 11289, 211591, 5, 2 ]
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
[ 0, 81, 40, 190, 190, 0, 0, 0, 0, 0, 0, 102, 252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 102, 252, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
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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

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Models trained or fine-tuned on yeajinmin/NER-News-BIDataset