NLP Modules for Text Preprocessing

Overview

In NLP pipelines, preprocessing Chinese and multilingual text efficiently is critical. The following modules provide tokenization, normalization, and conversion capabilities, covering most industrial use cases in search, recommendation, and analytics systems.

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Module Comparison

Module	Functionality	Language Support	Industrial Fit	Notes
Jieba	Chinese word segmentation	Simplified / Traditional	✔️ NLP preprocessing, search indexing	Dictionary + HMM segmentation, custom words
Hadar	Simplified ↔ Traditional conversion	Chinese (Simplified / Traditional)	✔️ Text normalization, batch conversion	Rule-driven, static dictionaries
SentencePiece	Subword tokenization	Multilingual / Unicode	✔️ Neural models, large-scale tokenization	BPE / Unigram LM, deterministic tokenization

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Recommendation

• Use Jieba for Chinese word segmentation in offline or online NLP pipelines.
• Use Hadar for deterministic conversion between Simplified and Traditional Chinese.
• Use SentencePiece for subword tokenization in neural network preprocessing or multilingual corpora.