Datasets:
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---
language:
- en
size_categories:
- 1M<n<10M
task_categories:
- token-classification
dataset_info:
- config_name: articles
features:
- name: title
dtype: string
- name: author
dtype: string
- name: datetime
dtype: string
- name: url
dtype: string
- name: month
dtype: string
- name: day
dtype: string
- name: doc_id
dtype: string
- name: text
dtype: string
- name: year
dtype: string
- name: doc_title
dtype: string
splits:
- name: train
num_bytes: 1313871812
num_examples: 446809
download_size: 791316510
dataset_size: 1313871812
- config_name: entities
features:
- name: doc_id
dtype: string
- name: sent_num
dtype: int32
- name: sentence
dtype: string
- name: doc_title
dtype: string
- name: spans
sequence:
- name: Score
dtype: float32
- name: Type
dtype: string
- name: Text
dtype: string
- name: BeginOffset
dtype: int32
- name: EndOffset
dtype: int32
- name: tags
struct:
- name: tokens
sequence: string
- name: raw_tags
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-DATE
'1': I-DATE
'2': L-DATE
'3': U-DATE
'4': B-DUC
'5': I-DUC
'6': L-DUC
'7': U-DUC
'8': B-EVE
'9': I-EVE
'10': L-EVE
'11': U-EVE
'12': B-LOC
'13': I-LOC
'14': L-LOC
'15': U-LOC
'16': B-MISC
'17': I-MISC
'18': L-MISC
'19': U-MISC
'20': B-ORG
'21': I-ORG
'22': L-ORG
'23': U-ORG
'24': B-PER
'25': I-PER
'26': L-PER
'27': U-PER
'28': B-QTY
'29': I-QTY
'30': L-QTY
'31': U-QTY
'32': B-TTL
'33': I-TTL
'34': L-TTL
'35': U-TTL
'36': O
splits:
- name: train
num_bytes: 3665237140
num_examples: 3515149
download_size: 967133582
dataset_size: 3665237140
configs:
- config_name: articles
data_files:
- split: train
path: articles/train-*
- config_name: entities
data_files:
- split: train
path: entities/train-*
---
# 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
```python
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:
```json
['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:
```json
{
"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:
```json
{'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:
```json
{'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/bloomberg_news_weak_ner},
}
```
|