LLMs can segment boundary-less languages directly from prompts, and the LLACA method distills this ability into a fast automaton that beats prior unsupervised Chinese word segmentation.
Chinese Word Segmentation: Another Decade Review (2007-2017)
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abstract
This paper reviews the development of Chinese word segmentation (CWS) in the most recent decade, 2007-2017. Special attention was paid to the deep learning technologies that has already permeated into most areas of natural language processing (NLP). The basic view we have arrived at is that compared to traditional supervised learning methods, neural network based methods have not shown any superior performance. The most critical challenge still lies on balancing of recognition of in-vocabulary (IV) and out-of-vocabulary (OOV) words. However, as neural models have potentials to capture the essential linguistic structure of natural language, we are optimistic about significant progresses may arrive in the near future.
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Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models
LLMs can segment boundary-less languages directly from prompts, and the LLACA method distills this ability into a fast automaton that beats prior unsupervised Chinese word segmentation.