State of the art Named Entity Recognition (NER) models have achieved an
impressive ability to extract common phrases from text that belong to labels
such as location, organization, time, and person. However, typical NER systems
that rely on having seen a specific entity in their training data in order to
label an entity perform poorly on rare or unseen entities ta in order to label
an entity perform poorly on rare or unseen entities (Derczynski et al., 2017).
This paper attempts to improve recognition of person names, a diverse category
that can grow any time someone is born or changes their name. In order for
downstream tasks to not exhibit bias based on cultural background, a model
should perform well on names from a variety of backgrounds. In this paper I
experiment with the training data and input structure of an English Bi-LSTM
name recognition model. I look at names from 103 countries to compare how well
the model performs on names from different cultures, specifically in the
context of a downstream task where extracted names will be matched to
information on file. I find that a model with combined character and word input
outperforms word-only models and may improve on accuracy compared to classical
NER models that are not geared toward identifying unseen entity values.

该研究旨在改进个人姓名的识别，在识别不同文化背景下的姓名方面，通过实验比较结合字符和词的输入模型与传统的 NER 模型，发现该模型表现更好且可能提高准确率。

跨文化姓名识别

Multicultural Name Recognition For Previously Unseen Names

Online reviews have become essential for users to make informed decisions in
everyday tasks ranging from planning summer vacations to purchasing groceries
and making financial investments. A key problem in using online reviews is the
overabundance of online that overwhelms the users. As a result, recommendation
systems for providing helpfulness of reviews are being developed. This paper
argues that cultural background is an important feature that impacts the nature
of a review written by the user, and must be considered as a feature in
assessing the helpfulness of online reviews. The paper provides an in-depth
study of differences in online reviews written by users from different cultural
backgrounds and how incorporating culture as a feature can lead to better
review helpfulness recommendations. In particular, we analyze online reviews
originating from two distinct cultural spheres, namely Arabic and Western
cultures, for two different products, hotels and books. Our analysis
demonstrates that the nature of reviews written by users differs based on their
cultural backgrounds and that this difference varies based on the specific
product being reviewed. Finally, we have developed six different review
helpfulness recommendation models that demonstrate that taking culture into
account leads to better recommendations.

本文针对在线评论过多令用户难以选择的问题，提出利用文化背景作为特征来评估在线评论的实用性，并通过对阿拉伯和西方文化圈中关于酒店和书籍的不同在线评论进行深入分析，发现基于文化背景评估评论可以提高推荐准确度，并开发了六个不同的评论推荐模型来证明这一点。

了解文化对在线评论有用性评估的影响

Understanding the Impact of Culture in Assessing Helpfulness of Online  Reviews

While cultural backgrounds have been shown to affect linguistic expressions,
existing natural language processing (NLP) research on culture modeling is
overly coarse-grained and does not examine cultural differences among speakers
of the same language. To address this problem and augment NLP models with
cultural background features, we collect, annotate, manually validate, and
benchmark EnCBP, a finer-grained news-based cultural background prediction
dataset in English. Through language modeling (LM) evaluations and manual
analyses, we confirm that there are noticeable differences in linguistic
expressions among five English-speaking countries and across four states in the
US. Additionally, our evaluations on nine syntactic (CoNLL-2003), semantic
(PAWS-Wiki, QNLI, STS-B, and RTE), and psycholinguistic tasks (SST-5, SST-2,
Emotion, and Go-Emotions) show that, while introducing cultural background
information does not benefit the Go-Emotions task due to text domain conflicts,
it noticeably improves deep learning (DL) model performance on other tasks. Our
findings strongly support the importance of cultural background modeling to a
wide variety of NLP tasks and demonstrate the applicability of EnCBP in
culture-related research.

本文介绍了如何通过 EnCBP 数据集中的语言建模评估和人工分析，证实英语五个说话国家和美国四个州之间存在明显的语言差异。在 CoNLL-2003、PAWS-Wiki、QNLI、STS-B、RTE、SST-5、SST-2、情感和 Go-Emotions 等任务上的评测表明，引入文化背景信息明显提高了深度学习模型在大多数任务上的性能。