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--- |
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language: |
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- en |
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size_categories: |
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- 1M<n<10M |
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task_categories: |
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- token-classification |
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dataset_info: |
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- config_name: articles |
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features: |
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- name: title |
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dtype: string |
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- name: author |
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dtype: string |
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- name: datetime |
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dtype: string |
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- name: url |
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dtype: string |
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- name: month |
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dtype: string |
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- name: day |
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dtype: string |
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- name: doc_id |
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dtype: string |
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- name: text |
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dtype: string |
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- name: year |
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dtype: string |
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- name: doc_title |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1313871812 |
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num_examples: 446809 |
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download_size: 791316510 |
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dataset_size: 1313871812 |
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- config_name: entities |
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features: |
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- name: doc_id |
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dtype: string |
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- name: sent_num |
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dtype: int32 |
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- name: sentence |
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dtype: string |
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- name: doc_title |
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dtype: string |
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- name: score |
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sequence: float32 |
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- name: entity_type |
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sequence: string |
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- name: entity_text |
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sequence: string |
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- name: start_char |
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sequence: int32 |
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- name: end_char |
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sequence: int32 |
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- name: tokens |
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sequence: string |
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- name: raw_tags |
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sequence: string |
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- name: ner_tags |
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sequence: |
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class_label: |
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names: |
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'0': B-DATE |
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'1': I-DATE |
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'2': L-DATE |
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'3': U-DATE |
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'4': B-DUC |
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'5': I-DUC |
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'6': L-DUC |
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'7': U-DUC |
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'8': B-EVE |
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'9': I-EVE |
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'10': L-EVE |
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'11': U-EVE |
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'12': B-LOC |
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'13': I-LOC |
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'14': L-LOC |
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'15': U-LOC |
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'16': B-MISC |
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'17': I-MISC |
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'18': L-MISC |
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'19': U-MISC |
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'20': B-ORG |
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'21': I-ORG |
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'22': L-ORG |
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'23': U-ORG |
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'24': B-PER |
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'25': I-PER |
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'26': L-PER |
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'27': U-PER |
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'28': B-QTY |
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'29': I-QTY |
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'30': L-QTY |
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'31': U-QTY |
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'32': B-TTL |
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'33': I-TTL |
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'34': L-TTL |
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'35': U-TTL |
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'36': O |
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splits: |
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- name: train |
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num_bytes: 3665237140 |
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num_examples: 3515149 |
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download_size: 966462235 |
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dataset_size: 3665237140 |
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configs: |
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- config_name: articles |
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data_files: |
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- split: train |
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path: articles/train-* |
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- config_name: entities |
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data_files: |
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- split: train |
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path: entities/train-* |
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--- |
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# Large Weak Labelled NER corpus |
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### Dataset Summary |
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The dataset is generated through weak labelling of the scraped and preprocessed news corpus (bloomberg's news). so, only to research purpose. |
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In order of the tokenization, news were splitted into sentences using `nltk.PunktSentenceTokenizer` (so, sometimes, tokenization might be not perfect) |
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### Usage |
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```python |
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from datasets import load_dataset |
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articles_ds = load_dataset("imvladikon/english_news_weak_ner", "articles") # just articles with metadata |
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entities_ds = load_dataset("imvladikon/english_news_weak_ner", "entities") |
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``` |
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#### NER tags |
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Tags description: |
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* O Outside of a named entity |
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* PER Person |
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* LOC Location |
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* ORG Organization |
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* MISC Miscellaneous |
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* DATE Date and time expression |
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* QTY Quantity |
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* EVE Event |
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* TTL Title |
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* DUC Commercial item |
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Tags: |
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```json |
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['B-DATE', 'I-DATE', 'L-DATE', 'U-DATE', 'B-DUC', 'I-DUC', 'L-DUC', 'U-DUC', 'B-EVE', 'I-EVE', 'L-EVE', 'U-EVE', 'B-LOC', 'I-LOC', 'L-LOC', 'U-LOC', 'B-MISC', 'I-MISC', 'L-MISC', 'U-MISC', 'B-ORG', 'I-ORG', 'L-ORG', 'U-ORG', 'B-PER', 'I-PER', 'L-PER', 'U-PER', 'B-QTY', 'I-QTY', 'L-QTY', 'U-QTY', 'B-TTL', 'I-TTL', 'L-TTL', 'U-TTL', 'O'] |
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``` |
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Tags statistics: |
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```json |
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{ |
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"O": 281586813, |
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"B-QTY": 2675754, |
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"L-QTY": 2675754, |
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"I-QTY": 2076724, |
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"U-ORG": 1459628, |
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"I-ORG": 1407875, |
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"B-ORG": 1318711, |
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"L-ORG": 1318711, |
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"B-PER": 1254037, |
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"L-PER": 1254037, |
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"U-MISC": 1195204, |
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"U-LOC": 1084052, |
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"U-DATE": 1010118, |
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"B-DATE": 919815, |
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"L-DATE": 919815, |
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"I-DATE": 650064, |
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"U-PER": 607212, |
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"U-QTY": 559523, |
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"B-LOC": 425431, |
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"L-LOC": 425431, |
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"I-PER": 262887, |
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"I-LOC": 201532, |
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"I-MISC": 190576, |
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"B-MISC": 162978, |
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"L-MISC": 162978, |
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"I-TTL": 64641, |
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"B-TTL": 53330, |
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"L-TTL": 53330, |
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"B-EVE": 43329, |
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"L-EVE": 43329, |
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"U-TTL": 41568, |
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"I-EVE": 35316, |
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"U-DUC": 33457, |
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"U-EVE": 19103, |
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"I-DUC": 15622, |
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"B-DUC": 15580, |
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"L-DUC": 15580 |
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} |
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``` |
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#### Sample: |
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Articles: |
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```json |
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{'title': 'Watson Reports Positive Findings for Prostate Drug', |
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'author': 'RobertSimison', |
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'datetime': '2007-01-16T14:16:56Z', |
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'url': 'http://www.bloomberg.com/news/2007-01-16/watson-reports-positive-findings-for-prostate-drug-update1-.html', |
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'month': '1', |
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'day': '16', |
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'doc_id': 'a5c7c556bd112ac22874492c4cdb18eb46a30905', |
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'text': 'Watson Pharmaceuticals Inc. (WPI) , the\nlargest U.S. maker of generic drugs, reported positive results\nfor its experimental prostate treatment in two late-state trials. \n The drug, silodosin, was more effective than a placebo in\ntreating enlarged prostates, or benign prostatic hyperplasia, the\nCorona, California-based company said today in a statement on PR\nNewswire. The tests were in the final of three phases of trials\nnormally needed for regulatory approval. \n Non-cancerous enlarged prostate affects more than half of\nAmerican men in their 60s and as many as 90 percent of them by\nage 85, Watson said. Prescription drug sales to treat the\ndisorder total $1.7 billion a year, the company said. \n Watson plans to apply for U.S. approval to market the drug\nin the first half of 2008, after completion later this year of a\none-year safety trial, the company said. The two studies reported\ntoday showed that cardiovascular and blood-pressure side effects\nwere low, Watson said. \n To contact the reporter on this story:\nRobert Simison in Washington at \n [email protected] . \n To contact the editor responsible for this story:\nRobert Simison at [email protected] .', |
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'year': '2007', |
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'doc_title': 'watson-reports-positive-findings-for-prostate-drug-update1-'} |
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``` |
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Entities: |
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```json |
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{'doc_id': '806fe637ed51e03d9ef7a8889fc84f63f8fc8569', |
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'sent_num': 9, |
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'sentence': 'Spain and Portugal together accounted for 45\npercent of group profit in 2010.', |
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'doc_title': 'bbva-may-post-lower-first-quarter-profit-hurt-by-spain-decline', |
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'spans': {'Score': [0.7858654856681824, |
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0.7856822609901428, |
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0.9990736246109009, |
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0.999079704284668], |
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'Type': ['ORGANIZATION', 'ORGANIZATION', 'QUANTITY', 'DATE'], |
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'Text': ['Spain', 'Portugal', '45\npercent', '2010'], |
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'BeginOffset': [0, 10, 42, 72], |
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'EndOffset': [5, 18, 52, 76]}, |
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'tags': {'tokens': ['Spain', |
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'Spain', |
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'and', |
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'Portugal', |
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'Spain', |
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'and', |
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'Portugal', |
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'together', |
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'accounted', |
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'for', |
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'45', |
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'\n', |
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'percent', |
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'Spain', |
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'and', |
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'Portugal', |
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'together', |
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'accounted', |
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'for', |
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'45', |
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'\n', |
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'percent', |
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'of', |
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'group', |
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'profit', |
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'in', |
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'2010', |
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'.'], |
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'raw_tags': ['U-ORG', |
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'O', |
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'O', |
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'U-ORG', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'B-QTY', |
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'I-QTY', |
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'L-QTY', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'O', |
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'U-DATE', |
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'O'], |
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'ner_tags': [23, |
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36, |
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36, |
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23, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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28, |
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29, |
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30, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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36, |
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3, |
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36]}} |
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``` |
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### Data splits |
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| name |train| |
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|---------|----:| |
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|entities|3515149| |
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|articles|446809| |
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### Citation Information |
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``` |
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@misc{imvladikon2023bb_news_weak_ner, |
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author = {Gurevich, Vladimir}, |
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title = {Weakly Labelled Large English NER corpus}, |
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year = {2022}, |
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howpublished = \url{https://huggingface.co/datasets/imvladikon/english_news_weak_ner}, |
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} |
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``` |
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