  Recently, these has been a surge on studying how to obtain partially
annotated data for model supervision. However, there still lacks a systematic
study on how to train statistical models with partial annotation (PA). Taking
dependency parsing as our case study, this paper describes and compares two
straightforward approaches for three mainstream dependency parsers. The first
approach is previously proposed to directly train a log-linear graph-based
parser (LLGPar) with PA based on a forest-based objective. This work for the
first time proposes the second approach to directly training a linear
graph-based parse (LGPar) and a linear transition-based parser (LTPar) with PA
based on the idea of constrained decoding. We conduct extensive experiments on
Penn Treebank under three different settings for simulating PA, i.e., random
dependencies, most uncertain dependencies, and dependencies with divergent
outputs from the three parsers. The results show that LLGPar is most effective
in learning from PA and LTPar lags behind the graph-based counterparts by large
margin. Moreover, LGPar and LTPar can achieve best performance by using LLGPar
to complete PA into full annotation (FA).
