  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.
