We present a methodology to support the analysis of culture from text such as
news events and demonstrate its usefulness on categorizing news events from
different categories (society, business, health, recreation, science, shopping,
sports, arts, computers, games and home) across different geographical
locations (different places in 117 countries). We group countries based on the
culture that they follow and then filter the news events based on their content
category. The news events are automatically labelled with the help of Hofstedes
cultural dimensions. We present combinations of events across different
categories and check the performances of different classification methods. We
also presents experimental comparison of different number of features in order
to find a suitable set to represent the culture.

本文提出了一种从文本中支持文化分析的方法，通过不同地理位置中不同类别的新闻事件（社会、商业、健康、娱乐、科学、购物、体育、艺术、计算机、游戏和家庭）的分类，演示了该方法的实用性。我们根据不同国家 / 地区遵循的文化将国家 / 地区进行分组，并根据其内容类别筛选新闻事件。然后利用 Hofstede 的文化维度自动标记新闻事件，并提供不同分类方法的表现，同时也比较了不同特征数量，以找到适合表示文化的特征集。

跨文化新闻事件分类

Classification of Cross-cultural News Events

Word embeddings have gained significant attention as learnable
representations of semantic relations between words, and have been shown to
improve upon the results of traditional word representations. However, little
effort has been devoted to using embeddings for the retrieval of entity
associations beyond pairwise relations. In this paper, we use popular embedding
methods to train vector representations of an entity-annotated news corpus, and
evaluate their performance for the task of predicting entity participation in
news events versus a traditional word cooccurrence network as a baseline. To
support queries for events with multiple participating entities, we test a
number of combination modes for the embedding vectors. While we find that even
the best combination modes for word embeddings do not quite reach the
performance of the full cooccurrence network, especially for rare entities, we
observe that different embedding methods model different types of relations,
thereby indicating the potential for ensemble methods.

本研究探讨了基于流行的嵌入方法训练矢量表示的实体注释新闻语料库的应用，以预测实体参与新闻事件，并测试了一些嵌入矢量的组合模式以支持多参与实体事件查询。我们发现，虽然单一的基于词嵌入的组合模式并不能完全达到传统词共现网络的水平，但不同的嵌入方法对不同类型的关系进行了建模，因此有可能实现集成方法。