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。