We present skweak, a versatile, Python-based software toolkit enabling NLP
developers to apply weak supervision to a wide range of NLP tasks. Weak
supervision is an emerging machine learning paradigm based on a simple idea:
instead of labelling data points by hand, we use labelling functions derived
from domain knowledge to automatically obtain annotations for a given dataset.
The resulting labels are then aggregated with a generative model that estimates
the accuracy (and possible confusions) of each labelling function. The skweak
toolkit makes it easy to implement a large spectrum of labelling functions
(such as heuristics, gazetteers, neural models or linguistic constraints) on
text data, apply them on a corpus, and aggregate their results in a fully
unsupervised fashion. skweak is especially designed to facilitate the use of
weak supervision for NLP tasks such as text classification and sequence
labelling. We illustrate the use of skweak for NER and sentiment analysis.
skweak is released under an open-source license and is available at:
this https URL

skweak 是一个基于 Python 的工具包，可应用于各种 NLP 任务，支持弱标注范式，可以使用从领域知识中获得的标注函数自动获取给定数据集的注释，并使用生成模型将结果聚合，以完全无监督的方式应用各种标注函数对文本数据进行分类和序列标注，尤其支持实体识别和情感分析等 NLP 任务。

skweak: NLP 弱监督简单实现

skweak: Weak Supervision Made Easy for NLP

Named Entity Recognition (NER) performance often degrades rapidly when
applied to target domains that differ from the texts observed during training.
When in-domain labelled data is available, transfer learning techniques can be
used to adapt existing NER models to the target domain. But what should one do
when there is no hand-labelled data for the target domain? This paper presents
a simple but powerful approach to learn NER models in the absence of labelled
data through weak supervision. The approach relies on a broad spectrum of
labelling functions to automatically annotate texts from the target domain.
These annotations are then merged together using a hidden Markov model which
captures the varying accuracies and confusions of the labelling functions. A
sequence labelling model can finally be trained on the basis of this unified
annotation. We evaluate the approach on two English datasets (CoNLL 2003 and
news articles from Reuters and Bloomberg) and demonstrate an improvement of
about 7 percentage points in entity-level $F_1$ scores compared to an
out-of-domain neural NER model.

文章提出了一种简单但有效的方法，通过弱监督机制在没有标注数据的情况下学习 NER 模型，该方法使用广泛的标注功能对目标域中的文本进行自动注释，然后通过隐马尔可夫模型将这些注释合并在一起，从而最终基于这个统一的注释训练一个序列标注模型。通过在两个英文数据集上的评估（CoNLL 2003 和来自路透社和彭博社的新闻文章），相比于域外神经 NER 模型，在实体级别的 F1 得分上取得了约 7 个百分点的提升。