Nigerian English adaptation, Pidgin, has evolved over the years through
multi-language code switching, code mixing and linguistic adaptation. While
Pidgin preserves many of the words in the normal English language corpus, both
in spelling and pronunciation, the fundamental meaning of these words have
changed significantly. For example,'ginger' is not a plant but an expression of
motivation and 'tank' is not a container but an expression of gratitude. The
implication is that the current approach of using direct English sentiment
analysis of social media text from Nigeria is sub-optimal, as it will not be
able to capture the semantic variation and contextual evolution in the
contemporary meaning of these words. In practice, while many words in Nigerian
Pidgin adaptation are the same as the standard English, the full English
language based sentiment analysis models are not designed to capture the full
intent of the Nigerian pidgin when used alone or code-mixed. By augmenting
scarce human labelled code-changed text with ample synthetic code-reformatted
text and meaning, we achieve significant improvements in sentiment scoring. Our
research explores how to understand sentiment in an intrasentential code mixing
and switching context where there has been significant word localization.This
work presents a 300 VADER lexicon compatible Nigerian Pidgin sentiment tokens
and their scores and a 14,000 gold standard Nigerian Pidgin tweets and their
sentiments labels.

本研究探讨了如何在 Pidgin 语境下理解情感分析，提出了一个 300 词汇表和 14,000 条推文的 Nigerian Pidgin 情感标签数据集，指出了将传统英语情感分析模型直接用于 Nigeria Pidgin 文本的缺陷。

用于上下文情感分类的尼日利亚派金英语语义丰富化

Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment  Classification

Treebank translation is a promising method for cross-lingual transfer of
syntactic dependency knowledge. The basic idea is to map dependency arcs from a
source treebank to its target translation according to word alignments. This
method, however, can suffer from imperfect alignment between source and target
words. To address this problem, we investigate syntactic transfer by code
mixing, translating only confident words in a source treebank. Cross-lingual
word embeddings are leveraged for transferring syntactic knowledge to the
target from the resulting code-mixed treebank. Experiments on University
Dependency Treebanks show that code-mixed treebanks are more effective than
translated treebanks, giving highly competitive performances among
cross-lingual parsing methods.

该研究探讨了使用代码混合的方式进行多语言句法依存关系知识传递的方法，并利用跨语言词向量在目标语言中转移句法知识的效果，结果显示代码混合的方法比传统的翻译方式更加有效。