  Existing disambiguation strategies for partial structured output learning
just cannot generalize well to solve the problem that there are some candidates
which can be false positive or similar to the ground-truth label. In this
paper, we propose a novel weak disambiguation for partial structured output
learning (WD-PSL). First, a piecewise large margin formulation is generalized
to partial structured output learning, which effectively avoids handling large
number of candidate structured outputs for complex structures. Second, in the
proposed weak disambiguation strategy, each candidate label is assigned with a
confidence value indicating how likely it is the true label, which aims to
reduce the negative effects of wrong ground-truth label assignment in the
learning process. Then two large margins are formulated to combine two types of
constraints which are the disambiguation between candidates and non-candidates,
and the weak disambiguation for candidates. In the framework of alternating
optimization, a new 2n-slack variables cutting plane algorithm is developed to
accelerate each iteration of optimization. The experimental results on several
sequence labeling tasks of Natural Language Processing show the effectiveness
of the proposed model.

Computation and Language;Machine Learning