SALMA, the first Arabic sense-annotated corpus, consists of ~34K tokens,
which are all sense-annotated. The corpus is annotated using two different
sense inventories simultaneously (Modern and Ghani). SALMA novelty lies in how
tokens and senses are associated. Instead of linking a token to only one
intended sense, SALMA links a token to multiple senses and provides a score to
each sense. A smart web-based annotation tool was developed to support scoring
multiple senses against a given word. In addition to sense annotations, we also
annotated the corpus using six types of named entities. The quality of our
annotations was assessed using various metrics (Kappa, Linear Weighted Kappa,
Quadratic Weighted Kappa, Mean Average Error, and Root Mean Square Error),
which show very high inter-annotator agreement. To establish a Word Sense
Disambiguation baseline using our SALMA corpus, we developed an end-to-end Word
Sense Disambiguation system using Target Sense Verification. We used this
system to evaluate three Target Sense Verification models available in the
literature. Our best model achieved an accuracy with 84.2% using Modern and
78.7% using Ghani. The full corpus and the annotation tool are open-source and
publicly available at this https URL

SALMA 是第一个阿拉伯语的语义注释语料库，包含约 34K 个令牌，所有令牌都进行了语义注释，并使用两个不同的语义库（现代语义库和 Ghani 语义库）进行注释。该语料库的创新之处在于如何关联令牌和语义，SALMA 将令牌与多个语义关联并为每个语义提供分数。我们开发了一个智能的基于 web 的注释工具来支持给定词语的多个语义的评分。除了语义注释，我们还使用六种类型的命名实体进行了语料库的注释。通过使用多种度量标准（Kappa，线性加权 Kappa，二次加权 Kappa，均方误差和平均平方根误差），我们评估了注释的质量，结果显示极高的注释者一致性。为了建立使用我们 SALMA 语料库的词义消歧基线，我们使用目标语义验证构建了一个端到端的词义消歧系统。我们使用该系统评估了文献中提供的三个目标语义验证模型。我们的最佳模型在使用现代语义库时达到了 84.2％的准确率，在使用 Ghani 语义库时达到了 78.7％的准确率。完整的语料库和注释工具是开源和公开可用的。

SALMA: 阿拉伯语义标注语料库和 WSD 基准测试

SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks

The paper describes the results of the first shared task on word sense
induction (WSI) for the Russian language. While similar shared tasks were
conducted in the past for some Romance and Germanic languages, we explore the
performance of sense induction and disambiguation methods for a Slavic language
that shares many features with other Slavic languages, such as rich morphology
and virtually free word order. The participants were asked to group contexts of
a given word in accordance with its senses that were not provided beforehand.
For instance, given a word "bank" and a set of contexts for this word, e.g.
"bank is a financial institution that accepts deposits" and "river bank is a
slope beside a body of water", a participant was asked to cluster such contexts
in the unknown in advance number of clusters corresponding to, in this case,
the "company" and the "area" senses of the word "bank". For the purpose of this
evaluation campaign, we developed three new evaluation datasets based on sense
inventories that have different sense granularity. The contexts in these
datasets were sampled from texts of Wikipedia, the academic corpus of Russian,
and an explanatory dictionary of Russian. Overall, 18 teams participated in the
competition submitting 383 models. Multiple teams managed to substantially
outperform competitive state-of-the-art baselines from the previous years based
on sense embeddings.

这篇研究论文描述了第一个关于俄语词义归纳（WSI）的共享任务的结果，使用 sense embeddings 方法，创新性的从维基百科、学术语料库和俄罗斯解释词典中开发三个新的评估数据集，为具有许多斯拉夫语言特征的斯拉夫语言探索词义归纳和消歧方法等主题。