  This work presents a fine-grained, text-chunking algorithm designed for the
task of multiword expressions (MWEs) segmentation. As a lexical class, MWEs
include a wide variety of idioms, whose automatic identification are a
necessity for the handling of colloquial language. This algorithm's core
novelty is its use of non-word tokens, i.e., boundaries, in a bottom-up
strategy. Leveraging boundaries refines token-level information, forging
high-level performance from relatively basic data. The generality of this
model's feature space allows for its application across languages and domains.
Experiments spanning 19 different languages exhibit a broadly-applicable,
state-of-the-art model. Evaluation against recent shared-task data places text
partitioning as the overall, best performing MWE segmentation algorithm,
covering all MWE classes and multiple English domains (including user-generated
text). This performance, coupled with a non-combinatorial, fast-running design,
produces an ideal combination for implementations at scale, which are
facilitated through the release of open-source software.
