We study the problem of building entity tagging systems by using a few rules
as weak supervision. Previous methods mostly focus on disambiguation entity
types based on contexts and expert-provided rules, while assuming entity spans
are given. In this work, we propose a novel method TALLOR that bootstraps
high-quality logical rules to train a neural tagger in a fully automated
manner. Specifically, we introduce compound rules that are composed from simple
rules to increase the precision of boundary detection and generate more diverse
pseudo labels. We further design a dynamic label selection strategy to ensure
pseudo label quality and therefore avoid overfitting the neural tagger.
Experiments on three datasets demonstrate that our method outperforms other
weakly supervised methods and even rivals a state-of-the-art distantly
supervised tagger with a lexicon of over 2,000 terms when starting from only 20
simple rules. Our method can serve as a tool for rapidly building taggers in
emerging domains and tasks. Case studies show that learned rules can
potentially explain the predicted entities.

本文通过使用少量规则进行弱监督来构建实体标记系统的问题，并提出了一种新的方法 TALLOR，该方法使用高质量的逻辑规则来训练神经标记器， 以完全自动化的方式提高准确性，同时避免神经标记器的过度拟合。

可学习逻辑规则的弱监督命名实体识别

Weakly Supervised Named Entity Tagging with Learnable Logical Rules

Political campaigns are full of political ads posted by candidates on social
media. Political advertisements constitute a basic form of campaigning,
subjected to various social requirements. We present the first publicly open
dataset for detecting specific text chunks and categories of political
advertising in the Polish language. It contains 1,705 human-annotated tweets
tagged with nine categories, which constitute campaigning under Polish
electoral law. We achieved a 0.65 inter-annotator agreement (Cohen's kappa
score). An additional annotator resolved the mismatches between the first two
annotators improving the consistency and complexity of the annotation process.
We used the newly created dataset to train a well established neural tagger
(achieving a 70% percent points F1 score). We also present a possible direction
of use cases for such datasets and models with an initial analysis of the
Polish 2020 Presidential Elections on Twitter.

在社交媒体上，政治竞选充斥着政治广告。该研究提出了一种公开可用的数据集，用于检测特定文本块和政治广告类别的内容，特定文本块和政治广告类别是符合波兰选举法的竞选基本形式。数据集包含 1,705 条人工标注的推文，标有九个类别。该数据集被用来训练神经标记器，并用于对波兰 2020 年总统选举在 Twitter 上的初步分析。