For automatically identifying hate speech and offensive content in tweets, a
system based on a classical supervised algorithm only fed with character
n-grams, and thus completely language-agnostic, is proposed by the SATLab team.
After its optimization in terms of the feature weighting and the classifier
parameters, it reached, in the multilingual HASOC 2021 challenge, a medium
performance level in English, the language for which it is easy to develop deep
learning approaches relying on many external linguistic resources, but a far
better level for the two less resourced language, Hindi and Marathi. It ends
even first when performances are averaged over the three tasks in these
languages, outperforming many deep learning approaches. These performances
suggest that it is an interesting reference level to evaluate the benefits of
using more complex approaches such as deep learning or taking into account
complementary resources.

通过使用基于字符 n-gram 的经典监督算法，SATLab 团队提出了一种自动识别推文中仇恨言论和冒犯性内容的系统，该系统与语言无关。在优化特征加权和分类器参数后，该系统在英语中达到了中等表现水平，在印地语和马拉地语这两种资源较少的语言中表现更好，甚至在这些语言的三项任务中的平均表现要比许多深度学习方法更好。表现表明该方法是一个有趣的参考水平，可用于评估使用更复杂方法（例如深度学习或考虑补充资源）的好处。

一种简单的、与语言无关的却非常强大的基线系统，用于仇恨言论和攻击性内容的识别

A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification

We propose a supervised algorithm for generating type embeddings in the same
semantic vector space as a given set of entity embeddings. The algorithm is
agnostic to the derivation of the underlying entity embeddings. It does not
require any manual feature engineering, generalizes well to hundreds of types
and achieves near-linear scaling on Big Graphs containing many millions of
triples and instances by virtue of an incremental execution. We demonstrate the
utility of the embeddings on a type recommendation task, outperforming a
non-parametric feature-agnostic baseline while achieving 15x speedup and
near-constant memory usage on a full partition of DBpedia. Using
state-of-the-art visualization, we illustrate the agreement of our
extensionally derived DBpedia type embeddings with the manually curated domain
ontology. Finally, we use the embeddings to probabilistically cluster about 4
million DBpedia instances into 415 types in the DBpedia ontology.

提出了一种监督算法来生成类型嵌入，该算法与给定实体嵌入集在相同的语义向量空间中工作。使用最先进的可视化技术，展示了算法在 DBpedia 本体论中的效果。最终，我们使用这些 embedding 将大约 4 百万个 DBpedia 实例概率聚类到 415 种类型。