This paper proposes a fast and simple unsupervised word segmentation algorithm that
utilizes the local predictability of adjacent character sequences, while searching for a least …

Word segmentation is an essential first step in the processing of far east asian languages
(ie, Chinese, Japanese, and Korean), which heavily influences subsequent processes such …

We propose an unsupervised word segmentation model, in which for each unlabelled
sentence sample, the learning objective is to maximize the generation probability of the …

Automatic word segmentation for the Chinese language is a fundamental and difficult
problem in the field of computer Chinese language information processing. This paper …

Recently, neural network models for natural language processing tasks have been
increasingly focused on for their ability of alleviating the burden of manual feature …

This paper describes the model we designed for the word segmentation bakeoff on Chinese
micro-blog data in the 2nd CIPS-SIGHAN joint conference on Chinese language processing …

We propose a simple two-level hierarchical probability model for unsupervised word
segmentation. By treating words as strings composed of morphemes/phonemes which are …

This paper presents a trainable rule-based algorithm for performing word segmentation. The
algorithm provides a simple, language-independent alternative to large-scale lexicai-based …

Word segmentation, the problem of finding word boundaries in speech, is of interest for a
range of tasks. Previous papers have suggested that for sequence-to-sequence models …

Currently most of state-of-the-art methods for Chinese word segmentation are based on
supervised learning, whose features are mostly extracted from a local context. These …