Abstract
Unsupervised constituency parsing is about identifying word sequences that form a syntactic unit (i.e., constituents) in a target sentence. Linguists identify the constituent by evaluating a set of Predicate-Argument Structure (PAS) equivalent sentences where we find the constituent corresponds to frequent word sequences. However, such information is unavailable to previous parsing methods which identify the constituent by observing sentences with diverse PAS. In this study, we empirically verify that \textbf{constituents correspond to word sequence patterns in the PAS-equivalent sentence set}. We propose a frequency-based method \emph{span-overlap}, applying the word sequence pattern to computational unsupervised parsing for the first time. Parsing experiments show that the span-overlap parser outperforms state-of-the-art parsers in eight out of ten languages. Further discrimination analysis confirms that the span-overlap method can non-trivially separate constituents from non-constituents. This result highlights the utility of the word sequence pattern. Additionally, we discover a multilingual phenomenon: \textbf{participant-denoting constituents are more frequent than event-denoting constituents}. The phenomenon indicates a behavioral difference between the two constituent types, laying the foundation for future labeled unsupervised parsing.
Abstract (translated)
无监督的句法分析是关于在目标句子中识别词序列形成语义单位的。语言学家通过评估一组等价于命题-论证结构（PAS）的句子，其中我们找到相应的词序列，来确定语素。然而，这样的信息对于先前通过观察具有多样PAS的句子的方法是不可用的。在这项研究中，我们通过实验验证了语素对应于PAS等价句子集中的词序列模式。我们提出了一个基于频率的方法（span-overlap），这是对计算无监督解析首次应用词序列模式。解析实验证明，在大多数语言中，跨度重叠解析器优于最先进的解析器。进一步的区分分析证实了跨度重叠方法可以非平凡地从非成分中区分出成分。这一结果突出了词序列模式的实用性。此外，我们还发现了一个多语言现象：表示参与者的成分比表示事件的成分更常见。这一现象表明了两种语素类型之间的行为差异，为未来的有标签无监督解析奠定了基础。
URL
https://arxiv.org/abs/2404.12059
