An ability that underlies human syntactic knowledge is determining which
words can appear in the similar structures (i.e. grouping words by their
syntactic categories). These groupings enable humans to combine structures in
order to communicate complex meanings. A foundational question is how do
children acquire this ability underlying syntactic knowledge. In exploring this
process, we will review various engineering approaches whose goal is similar to
that of a child's -- without prior syntactic knowledge, correctly identify the
parts of speech (POS) of the words in a sample of text. In reviewing these
unsupervised tagging efforts, we will discuss common themes that support the
advances in the models and their relevance for language acquisition. For
example, we discuss how each model judges success (evaluation metrics), the
"additional information" that constrains the POS learning (such as orthographic
information), and the context used to determine POS (only previous word, words
before and after the target, etc). The identified themes pave the way for
future investigations into the cognitive processes that underpin the
acquisition of syntactic categories and provide a useful layout of current
state of the art unsupervised POS tagging models.

人们如何获取句法类别知识是基础性问题，本研究回顾了各种工程方法，目的在于无需先验句法知识就能正确识别样本文本中的词性，并讨论了模型的评估、约束因素以及上下文对词性的确定等共同主题。这些主题为深入研究认知过程奠定了基础，并提供了当前无监督词性标注模型的有用参考。

无监督词性标注的综述及其对语言习得的启示

Review of Unsupervised POS Tagging and Its Implications on Language  Acquisition

Although word-level prosody modeling in neural text-to-speech (TTS) has been
investigated in recent research for diverse speech synthesis, it is still
challenging to control speech synthesis manually without a specific reference.
This is largely due to lack of word-level prosody tags. In this work, we
propose a novel approach for unsupervised word-level prosody tagging with two
stages, where we first group the words into different types with a decision
tree according to their phonetic content and then cluster the prosodies using
GMM within each type of words separately. This design is based on the
assumption that the prosodies of different type of words, such as long or short
words, should be tagged with different label sets. Furthermore, a TTS system
with the derived word-level prosody tags is trained for controllable speech
synthesis. Experiments on LJSpeech show that the TTS model trained with
word-level prosody tags not only achieves better naturalness than a typical
FastSpeech2 model, but also gains the ability to manipulate word-level prosody.

提出了一种基于决策树和 GMM 的无监督词级韵律标记方法，利用这种方法训练的 TTS 系统可以实现可控语音合成。实验结果显示，与 typical FastSpeech2 模型相比，使用词级韵律标记的 TTS 模型不仅具有更好的自然度，而且还能够操作词级韵律。