Transition-based dependency parsers usually use transition systems that monotonically extend partial parse states until they identify a complete parse tree. Honnibal et al. (2013) showed that greedy onebest parsing accuracy can be improved by adding additional non-monotonic transitions that permit the parser to "repair" earlier parsing mistakes by "over-writing" earlier parsing decisions. This increases the size of the set of complete parse trees that each partial parse state can derive, enabling such a parser to escape the "garden paths" that can trap monotonic greedy transition-based dependency parsers.We describe a new set of non-monotonic transitions that permits a partial parse state to derive a larger set of completed parse trees than previous work, which allows our parser to escape from a larger set of garden paths. A parser with our new nonmonotonic transition system has 91.85% directed attachment accuracy, an improvement of 0.6% over a comparable parser using the standard monotonic arc-eager transitions.

We present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. For comparison, we evaluated two pipeline systems, using state-of-the-art disfluency detectors. The joint model performed better on both tasks, with a parse accuracy of 90.5% and 84.0% accuracy at disfluency detection. The model runs in expected linear time, and processes over 550 tokens a second.

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The vast majority of parser evaluation is conducted on the 1984 Wall Street Journal (WSJ). In-domain evaluation of this kind is important for system development, but gives little indication about how the parser will perform on many practical problems. Wikipedia is an interesting domain for parsing that has so far been underexplored. We present statistical parsing results that for the first time provide information about what sort of performance a user parsing Wikipedia text can expect. We find that the C&C parser's standard model is 4.3% less accurate on Wikipedia text, but that a simple self-training exercise reduces the gap to 3.8%. The self-training also speeds up the parser on newswire text by 20%.

The lack of a large annotated systemic functional grammar (SFG) corpus has posed a significant challenge for the development of the theory. Automating SFG annotation is challenging because the theory uses a minimal constituency model, allocating as much of the work as possible to a set of hierarchically organised features.In this paper we show that despite the unorthodox organisation of SFG, adapting existing resources remains the most practical way to create an annotated corpus. We present and analyse SFGBank, an automated conversion of the Penn Treebank into systemic functional grammar. The corpus is comparable to those available for other linguistic theories, offering many opportunities for new research.