The relationship between energy demand and variables such as economic
activity and weather is well established. However, this paper aims to explore
the connection between energy demand and other social aspects, which receive
little attention. Through the use of natural language processing on a large
news corpus, we shed light on this important link. This study was carried out
in five regions of the UK and Ireland and considers multiple horizons from 1 to
30 days. It also considers economic variables such as GDP, unemployment and
inflation. We found that: 1) News about military conflicts, transportation, the
global pandemic, regional economics, and the international energy market are
related to electricity demand. 2) Economic indicators are more important in the
East Midlands and Northern Ireland, while social indicators are more useful in
the West Midlands and the South West of England. 3) The use of these indices
improved forecasting performance by up to 9%.

通过对大量新闻语料库进行自然语言处理，本研究探讨了能源需求与社会其他方面的联系，并发现军事冲突、交通运输、全球大流行、地区经济和国际能源市场等因素与电力需求有关。此外，研究还发现在英国的五个地区，经济指标对于东米德兰兹和北爱尔兰而言更为重要，而社会指标对于西米德兰兹和英格兰西南部更为有用。此外，利用这些指标可以提高预测性能最多达到 9%。

经济和社会因素对能源需求的影响调查

Investigation of the Impact of Economic and Social Factors on Energy  Demand through Natural Language Processing

Intelligent writing assistants powered by large language models (LLMs) are
more popular today than ever before, but their further widespread adoption is
precluded by sub-optimal performance. In this position paper, we argue that a
major reason for this sub-optimal performance and adoption is a singular focus
on the information content of language while ignoring its social aspects. We
analyze the different dimensions of these social factors in the context of
writing assistants and propose their incorporation into building smarter, more
effective, and truly personalized writing assistants that would enrich the user
experience and contribute to increased user adoption.

本文通过分析针对信息内容而忽略其社会方面的单一视角，提出智能写作助手需要融入社会因素来建立更有效、更个性化的写作助手，以提高用户体验和促进用户的广泛采用。

写作辅助应建模语言的社会因素

Writing Assistants Should Model Social Factors of Language

Historic dress artifacts are a valuable source for human studies. In
particular, they can provide important insights into the social aspects of
their corresponding era. These insights are commonly drawn from garment
pictures as well as the accompanying descriptions and are usually stored in a
standardized and controlled vocabulary that accurately describes garments and
costume items, called the Costume Core Vocabulary. Building an accurate Costume
Core from garment descriptions can be challenging because the historic garment
items are often donated, and the accompanying descriptions can be based on
untrained individuals and use a language common to the period of the items. In
this paper, we present an approach to use Natural Language Processing (NLP) to
map the free-form text descriptions of the historic items to that of the
controlled vocabulary provided by the Costume Core. Despite the limited
dataset, we were able to train an NLP model based on the Universal Sentence
Encoder to perform this mapping with more than 90% test accuracy for a subset
of the Costume Core vocabulary. We describe our methodology, design choices,
and development of our approach, and show the feasibility of predicting the
Costume Core for unseen descriptions. With more garment descriptions still
being curated to be used for training, we expect to have higher accuracy for
better generalizability.

使用自然语言处理技术将历史服装物品的自由文本描述映射到服装核心词汇表，从而提高预测准确性。