Multilingual (non-English) NLP — 7 things to know before getting started
A primer on differences between English and Non-English NLP
Multilingual Natural Language Processing (NLP) is a rapidly growing field, but it is different from English NLP in several ways. It requires a deep understanding of multiple languages and their unique characteristics.
From the lack of space characters in some languages to the different cultural contexts, grammar and syntax, text direction, and complex structures, the challenges are multiple!
In this article, we will see 7 reasons why multilingual NLP is different from English NLP with relevant examples as necessary.
1. No spaces between words
Unlike English, languages like Chinese, Thai, and Japanese do not have spaces to separate words. This makes it more difficult for NLP algorithms to accurately segment text into individual words and phrases, which can impact the accuracy of NLP tasks.
For example, the sentence “今天天气很好” in Chinese translates to “Today the weather is good,” but there are no spaces between the words, making it difficult for an NLP system to split words without morpheme analysis.
