Although there is widespread recognition of racial bias in US law, it is
unclear how such bias appears in the language of law, namely judicial opinions,
and whether it varies across time period or region. Building upon approaches
for measuring implicit racial bias in large-scale corpora, we approximate GloVe
word embeddings for over 6 million US federal and state court cases from 1860
to 2009. We find strong evidence of racial bias across nearly all regions and
time periods, as traditionally Black names are more closely associated with
pre-classified "unpleasant" terms whereas traditionally White names are more
closely associated with pre-classified "pleasant" terms. We also test whether
legal opinions before 1950 exhibit more implicit racial bias than those after
1950, as well as whether opinions from Southern states exhibit less change in
racial bias than those from Northeastern states. We do not find evidence of
elevated bias in legal opinions before 1950, or evidence that legal opinions
from Northeastern states show greater change in racial bias over time compared
to Southern states. These results motivate further research into
institutionalized racial bias.

通过分析 1860 年到 2009 年超过 600 万份美国联邦和州法院案件的 GloVe 单词嵌入，研究发现几乎所有地区和时期都存在种族偏见现象，即传统黑人姓名更与 “不愉快” 术语相关，而传统白人姓名则更与 “愉悦” 术语相关，这些结果促进了对种族偏见的进一步研究。

美国法律意见书中种族偏见趋势

Racial Bias Trends in the Text of US Legal Opinions

In this work, we tackle the problem of Armenian named entity recognition,
providing silver- and gold-standard datasets as well as establishing baseline
results on popular models. We present a 163000-token named entity corpus
automatically generated and annotated from Wikipedia, and another 53400-token
corpus of news sentences with manual annotation of people, organization and
location named entities. The corpora were used to train and evaluate several
popular named entity recognition models. Alongside the datasets, we release
50-, 100-, 200-, 300-dimensional GloVe word embeddings trained on a collection
of Armenian texts from Wikipedia, news, blogs, and encyclopedia.

本文提供了基于维基百科、新闻句子等数据集训练的 Armenian 命名实体识别模型，以及 50~300 维的 GloVe 词向量模型，并给出相应实验结果。