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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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'O': 0, 'PS_NAME': 1, 'PS_CHARACTER': 2, 'PS_PET': 3, 'FD_SCIENCE': 4, 'FD_SOCIAL_SCIENCE': 5, 'FD_MEDICINE': 6, 'FD_ART':7, 'FD_HUMANITIES': 8, 'FD_OTHERS': 9, |
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'TR_SCIENCE': 10, 'TR_SOCIAL_SCIENCE': 11, 'TR_MEDICINE': 12, 'TR_ART': 13, 'TR_HUMANITIES': 14, 'TR_OTHERS': 15, 'AF_BUILDING': 16, 'AF_CULTURAL_ASSET': 17, 'AF_ROAD': 18, 'AF_TRANSPORT': 19, |
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'AF_MUSICAL_INSTRUMENT': 20, 'AF_WEAPON': 21, 'AFA_DOCUMENT': 22, 'AFA_PERFORMANCE': 23, 'AFA_VIDEO': 24, 'AFA_ART_CRAFT': 25, 'AFA_MUSIC': 26, "AFW_SERVICE_PRODUCTS": 27, 'AFW_OTHER_PRODUCTS': 28, 'OGG_ECONOMY': 29, |
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'OGG_EDUCATION': 30, 'OGG_MILITARY': 31, 'OGG_MEDIA': 32, 'OGG_SPORTS': 33, 'OGG_ART': 34, 'OGG_MEDICINE': 35, 'OGG_RELIGION': 36, 'OGG_SCIENCE': 37, 'OGG_LIBRARY':38, 'OGG_LAW': 39, |
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'OGG_POLITICS': 40, 'OGG_FOOD': 41, 'OGG_HOTEL': 42, 'OGG_OTHERS': 43, 'LCP_COUNTRY': 44, 'LCP_PROVINCE': 45, 'LCP_COUNTY':46, 'LCP_CITY': 47, 'LCP_CAPITALCITY': 48, 'LCG_RIVER': 49, |
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'LCG_OCEAN': 50,'LCG_BAY': 51, 'LCG_MOUNTAIN':52, 'LCG_ISLAND': 53, 'LCG_CONTINENT': 54, 'LC_SPACE': 55, 'LC_OTHERS': 56, 'CV_CULTURE': 57, 'CV_TRIBE': 58, 'CV_LANGUAGE': 59, |
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'CV_POLICY': 60, 'CV_LAW': 61, 'CV_CURRENCY': 62, 'CV_TAX': 63, 'CV_FUNDS': 64, 'CV_ART': 65, 'CV_SPORTS': 66, 'CV_SPORTS_POSITION': 67, 'CV_SPORTS_INST': 68, 'CV_PRIZE': 69, |
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'CV_RELATION': 70, 'CV_OCCUPATION': 71, 'CV_POSITION': 72, 'CV_FOOD': 73, 'CV_DRINK': 74, 'CV_FOOD_STYLE': 75, 'CV_CLOTHING': 76, 'CV_BUILDING_TYPE': 77, 'DT_DURATION': 78, 'DT_DAY': 79, |
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'DT_WEEK':80, 'DT_MONTH': 81, 'DT_YEAR': 82, 'DT_SEASON': 83, 'DT_GEOAGE': 84, 'DT_DYNASTY': 85, 'DT_OTHERS': 86, 'TI_DURATION': 87, 'TI_HOUR':88, 'TI_MINUTE': 89, |
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'TI_SECOND': 90, 'TI_OTHERS': 91, 'QT_AGE': 92, 'QT_SIZE': 93, 'QT_LENGTH': 94, 'QT_COUNT': 95, 'QT_MAN_COUNT': 96, 'QT_WEIGHT': 97, 'QT_PERCENTAGE': 98, 'QT_SPEED': 99, |
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'QT_TEMPERATURE': 100,'QT_VOLUME': 101, 'QT_ORDER': 102, 'QT_PRICE': 103, 'QT_PHONE': 104, 'QT_SPORTS': 105, 'QT_CHANNEL': 106, 'QT_ALBUM': 107, 'QT_ADDRESS': 108, 'QT_OTHERS': 109, |
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'EV_ACTIVITY': 110, 'EV_WAR_REVOLUTION': 111, 'EV_SPORTS': 112, 'EV_FESTIVAL': 113, 'EV_OTHERS': 114, 'AM_INSECT': 115, 'AM_BIRD': 116, 'AM_FISH': 117, 'AM_MAMMALIA': 118, 'AM_AMPHIBIA': 119, |
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'AM_REPTILIA': 120, 'AM_TYPE': 121, 'AM_PART': 122, 'AM_OTHERS': 123, 'PT_FRUIT': 124, 'PT_FLOWER': 125, 'PT_TREE': 126, 'PT_GRASS': 127, 'PT_TYPE': 128, 'PT_PART': 129, |
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'PT_OTHERS': 130, 'MT_ELEMENT': 131, 'MT_METAL': 132, 'MT_ROCK':133, 'MT_CHEMICAL': 134, 'TM_COLOR': 135, 'TM_DIRECTION': 136, 'TM_CLIMATE': 137, 'TM_SHAPE': 138, 'TM_CELL_TISSUE_ORGAN': 139, |
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'TMM_DISEASE': 140, 'TMM_DRUG': 141, 'TMI_HW':142, 'TMI_SW': 143, 'TMI_SITE': 144, 'TMI_EMAIL': 145, 'TMI_MODEL': 146, 'TMI_SERVICE': 147, 'TMI_PROJECT': 148, 'TMIG_GENRE': 149, 'TM_SPORTS': 150 |
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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 |