Mental health disorders significantly impact people globally, regardless of
background, education, or socioeconomic status. However, access to adequate
care remains a challenge, particularly for underserved communities with limited
resources. Text mining tools offer immense potential to support mental
healthcare by assisting professionals in diagnosing and treating patients. This
study addresses the scarcity of Arabic mental health resources for developing
such tools. We introduce MentalQA, a novel Arabic dataset featuring
conversational-style question-and-answer (QA) interactions. To ensure data
quality, we conducted a rigorous annotation process using a well-defined schema
with quality control measures. Data was collected from a question-answering
medical platform. The annotation schema for mental health questions and
corresponding answers draws upon existing classification schemes with some
modifications. Question types encompass six distinct categories: diagnosis,
treatment, anatomy \& physiology, epidemiology, healthy lifestyle, and provider
choice. Answer strategies include information provision, direct guidance, and
emotional support. Three experienced annotators collaboratively annotated the
data to ensure consistency. Our findings demonstrate high inter-annotator
agreement, with Fleiss' Kappa of $0.61$ for question types and $0.98$ for
answer strategies. In-depth analysis revealed insightful patterns, including
variations in question preferences across age groups and a strong correlation
between question types and answer strategies. MentalQA offers a valuable
foundation for developing Arabic text mining tools capable of supporting mental
health professionals and individuals seeking information.

本研究介绍了 MentalQA，一个新颖的阿拉伯语数据集，提供问题和答案的对话式交互。该数据集为开发阿拉伯语文本挖掘工具提供了基础，以支持心理健康专业人员和寻求信息的个人。

MentalQA：一个用于心理保健问题和回答的阿拉伯语语料库

MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental  Healthcare

Automated information extraction with natural language processing (NLP) tools
is required to gain systematic insights from the large number of COVID-19
publications, reports and social media posts, which far exceed human processing
capabilities. A key challenge for NLP is the extensive variation in terminology
used to describe medical entities, which was especially pronounced for this
newly emergent disease. Here we present an NLP toolbox comprising very large
English dictionaries of synonyms for SARS-CoV-2 (including variant names) and
COVID-19, which can be used with dictionary-based NLP tools. We also present a
silver standard corpus generated with the dictionaries, and a gold standard
corpus, consisting of PubMed abstracts manually annotated for disease, virus,
symptom, protein/gene, cell type, chemical and species terms, which can be used
to train and evaluate COVID-19-related NLP tools. Code for annotation, which
can be used to expand the silver standard corpus or for text mining is also
included. This toolbox is freely available on GitHub (on
this https URL) and zenodo
(this https URL). The toolbox can be used for a variety
of text analytics tasks related to the COVID-19 crisis and has already been
used to create a COVID-19 knowledge graph, study the variability and evolution
of COVID-19-related terminology and develop and benchmark text mining tools.

本文介绍了一种自然语言处理工具包，其包括疾病、病毒、症状、蛋白质 / 基因、细胞类型、化学和物种等字典，以及使用这些字典生成的标准语料库，可用于训练和评价 COVID-19 相关的 NLP 工具和文本挖掘。该工具包可以用于疫情相关的各种文本分析任务，并且已经被用于创建 COVID-19 知识图谱，研究 COVID-19 相关术语的可变性与演化以及开发和基准测试文本挖掘工具。

与 SARS-CoV-2 和 COVID-19 相关的生物医学自然语言处理的英语词典、金标准语料库和银标准语料库

English dictionaries, gold and silver standard corpora for biomedical  natural language processing related to SARS-CoV-2 and COVID-19

Capturing the semantics of related biological concepts, such as genes and
mutations, is of significant importance to many research tasks in computational
biology such as protein-protein interaction detection, gene-drug association
prediction, and biomedical literature-based discovery. Here, we propose to
leverage state-of-the-art text mining tools and machine learning models to
learn the semantics via vector representations (aka. embeddings) of over
400,000 biological concepts mentioned in the entire PubMed abstracts. Our
learned embeddings, namely BioConceptVec, can capture related concepts based on
their surrounding contextual information in the literature, which is beyond
exact term match or co-occurrence-based methods. BioConceptVec has been
thoroughly evaluated in multiple bioinformatics tasks consisting of over 25
million instances from nine different biological datasets. The evaluation
results demonstrate that BioConceptVec has better performance than existing
methods in all tasks. Finally, BioConceptVec is made freely available to the
research community and general public via
this https URL

本篇研究提出了 BioConceptVec，通过利用最先进的文本挖掘工具和机器学习模型学习 PubMed 摘要中介绍的超过 400,000 个生物概念的向量表示（即嵌入），来捕捉相关概念的语义。BioConceptVec 已经在包括 9 个不同生物数据集的 2500 万实例的多个生物信息学任务中得到了全面评估，在所有任务中其性能均优于现有方法。最后，BioConceptVec 通过网站免费向研究社区和公众提供。