imvladikon/english_news_weak_ner

Large Weak Labelled NER corpus

Dataset Summary

The dataset is generated through weak labelling of the scraped and preprocessed news corpus (bloomberg's news). so, only to research purpose. In order of the tokenization, news were splitted into sentences using nltk.PunktSentenceTokenizer (so, sometimes, tokenization might be not perfect)

Usage

from datasets import load_dataset

articles_ds = load_dataset("imvladikon/english_news_weak_ner", "articles") # just articles with metadata
entities_ds = load_dataset("imvladikon/english_news_weak_ner", "entities")

NER tags

Tags description:

• O Outside of a named entity
• PER Person
• LOC Location
• ORG Organization
• MISC Miscellaneous
• DATE Date and time expression
• QTY Quantity
• EVE Event
• TTL Title
• DUC Commercial item

Tags:

['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']

Tags statistics:

{
    "O": 281586813,
    "B-QTY": 2675754,
    "L-QTY": 2675754,
    "I-QTY": 2076724,
    "U-ORG": 1459628,
    "I-ORG": 1407875,
    "B-ORG": 1318711,
    "L-ORG": 1318711,
    "B-PER": 1254037,
    "L-PER": 1254037,
    "U-MISC": 1195204,
    "U-LOC": 1084052,
    "U-DATE": 1010118,
    "B-DATE": 919815,
    "L-DATE": 919815,
    "I-DATE": 650064,
    "U-PER": 607212,
    "U-QTY": 559523,
    "B-LOC": 425431,
    "L-LOC": 425431,
    "I-PER": 262887,
    "I-LOC": 201532,
    "I-MISC": 190576,
    "B-MISC": 162978,
    "L-MISC": 162978,
    "I-TTL": 64641,
    "B-TTL": 53330,
    "L-TTL": 53330,
    "B-EVE": 43329,
    "L-EVE": 43329,
    "U-TTL": 41568,
    "I-EVE": 35316,
    "U-DUC": 33457,
    "U-EVE": 19103,
    "I-DUC": 15622,
    "B-DUC": 15580,
    "L-DUC": 15580
}

Sample:

Articles:

{'title': 'Watson Reports Positive Findings for Prostate Drug',
 'author': 'RobertSimison',
 'datetime': '2007-01-16T14:16:56Z',
 'url': 'http://www.bloomberg.com/news/2007-01-16/watson-reports-positive-findings-for-prostate-drug-update1-.html',
 'month': '1',
 'day': '16',
 'doc_id': 'a5c7c556bd112ac22874492c4cdb18eb46a30905',
 '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 rsimison@bloomberg.net .  \n To contact the editor responsible for this story:\nRobert Simison at   rsimison@bloomberg.net .',
 'year': '2007',
 'doc_title': 'watson-reports-positive-findings-for-prostate-drug-update1-'}

Entities:

{'doc_id': '806fe637ed51e03d9ef7a8889fc84f63f8fc8569',
 'sent_num': 9,
 'sentence': 'Spain and Portugal together accounted for 45\npercent of group profit in 2010.',
 'doc_title': 'bbva-may-post-lower-first-quarter-profit-hurt-by-spain-decline',
 'spans': {'Score': [0.7858654856681824,
   0.7856822609901428,
   0.9990736246109009,
   0.999079704284668],
  'Type': ['ORGANIZATION', 'ORGANIZATION', 'QUANTITY', 'DATE'],
  'Text': ['Spain', 'Portugal', '45\npercent', '2010'],
  'BeginOffset': [0, 10, 42, 72],
  'EndOffset': [5, 18, 52, 76]},
 'tags': {'tokens': ['Spain',
   'Spain',
   'and',
   'Portugal',
   'Spain',
   'and',
   'Portugal',
   'together',
   'accounted',
   'for',
   '45',
   '\n',
   'percent',
   'Spain',
   'and',
   'Portugal',
   'together',
   'accounted',
   'for',
   '45',
   '\n',
   'percent',
   'of',
   'group',
   'profit',
   'in',
   '2010',
   '.'],
  'raw_tags': ['U-ORG',
   'O',
   'O',
   'U-ORG',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'B-QTY',
   'I-QTY',
   'L-QTY',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'O',
   'U-DATE',
   'O'],
  'ner_tags': [23,
   36,
   36,
   23,
   36,
   36,
   36,
   36,
   36,
   36,
   28,
   29,
   30,
   36,
   36,
   36,
   36,
   36,
   36,
   36,
   36,
   36,
   36,
   36,
   36,
   36,
   3,
   36]}}

Data splits

name	train
entities	3515149
articles	446809

Citation Information

@misc{imvladikon2023bb_news_weak_ner,
  author = {Gurevich, Vladimir},
  title = {Weakly Labelled Large English NER corpus},
  year = {2022},
  howpublished = \url{https://huggingface.co/datasets/imvladikon/english_news_weak_ner},
}