Social Network Analysis (SNA) is a set of techniques developed in the field
of social and behavioral sciences research, in order to characterize and study
the social relationships that are established among a set of individuals. When
building a social network for performing an SNA analysis, an initial process of
data gathering is achieved in order to extract the characteristics of the
individuals and their relationships. This is usually done by completing a
questionnaire containing different types of questions that will be later used
to obtain the SNA measures needed to perform the study. There are, then, a
great number of different possible network generating questions and also many
possibilities for mapping the responses to the corresponding characteristics
and relationships. Many variations may be introduced into these questions (the
way they are posed, the weights given to each of the responses, etc.) that may
have an effect on the resulting networks. All these different variations are
difficult to achieve manually, because the process is time-consuming and error
prone. The tool described in this paper uses semantic knowledge representation
techniques in order to facilitate this kind of sensitivity studies. The base of
the tool is a conceptual structure, called "ontology" that is able to represent
the different concepts and their definitions. The tool is compared to other
similar ones, and the advantages of the approach are highlighted, giving some
particular examples from an ongoing SNA study about alcohol consumption habits
in adolescents.

社交网络分析（SNA）是一组为社会和行为科学研究中开发的技术，用于描述和研究一组个体之间建立的社交关系。本文介绍了一种利用语义知识表示技术来促进这类敏感性研究的工具，以问卷调查为基础，用于提取个体特征和关系，相比其他类似工具具有优势，并提供了关于青少年饮酒习惯的正在进行的 SNA 研究的具体示例。

酒精消费研究中的敏感度分析和假设性测试的语义社交网络分析工具

A Semantic Social Network Analysis Tool for Sensitivity Analysis and  What-If Scenario Testing in Alcohol Consumption Studies

In Africa, and the world at large, there is an increasing focus on developing
Neural Machine Translation (NMT) systems to overcome language barriers. NMT for
Low-resource language is particularly compelling as it involves learning with
limited labelled data. However, obtaining a well-aligned parallel corpus for
low-resource languages can be challenging. The disparity between the
technological advancement of a few global languages and the lack of research on
NMT for local languages in Chad is striking. End-to-end NMT trials on
low-resource Chad languages have not been attempted. Additionally, there is a
dearth of online and well-structured data gathering for research in Natural
Language Processing, unlike some African languages. However, a guided approach
for data gathering can produce bitext data for many Chadian language
translation pairs with well-known languages that have ample data. In this
project, we created the first sba-Fr Dataset, which is a corpus of
Ngambay-to-French translations, and fine-tuned three pre-trained models using
this dataset. Our experiments show that the M2M100 model outperforms other
models with high BLEU scores on both original and original+synthetic data. The
publicly available bitext dataset can be used for research purposes.

该研究聚焦于低资源语言，特别关注尼日尔的低资源语言，制定了有效的数据收集方法，构建了第一个 sba-Fr 数据集，并对三个预训练模型进行了优化，结果显示 M2M100 模型在原始数据和原始 + 合成数据上都取得了较高 BLEU 分数，该公开数据集可用于研究目的。