written by Stephen Tratz and Eduard Hovy (Information Sciences Institute, University of Southern Carolina)

presented by Martin Popel

reported by Michal Novák

The paper describes a high-quality conversion of Penn Treebank to dependency trees. The authors introduce an improved labeled dependency scheme based on the Stanford's one. In addition, they extend the non-directional easy-first first algorithm of Goldberg and Elhadad to support non-projective trees by adding “move” actions inspired by Nivre's swap-based reordering for shift-reduce parsing. Their parser is capable of producing shallow semantic annotations for prepositions, possesives and noun compounds.

• in general, there are (at least) 3 possible types of dependency labels:
  • unlabeled - is it really a set of labels?
  • coarse labels of the CoNLL tasks
    • 10-20 labels
    • for example NMOD is always under a noun - it's an easy task and the result is not quite useful
  • their scheme is based on the Stanford's dependency labels

• converting phrase trees of Penn Treebank to dependency ones
• it consists of 3 steps:
  1. add structure to flat NPs
  2. constituent-to-dependency converter with some head-finding rule modifications
     • a list of rules in Figure 2 is hardly understandable without reading a paper their conversion method is related to
     • they reduced the number of generic “dep/DEP” relation
       • Stanford tags are hierarchical and “dep/DEP” is the top-most one
     • correcting of POS using the syntactic info + additional rules for specific word forms
     • 1.3% of arcs are non-projective (out of 8.1% of all non-projective arcs) because of the following conversion (agreement can be a motivation for this, i.e. in Czech):
  3. additional changes and the final conversion from the intermediate output to a dependency structure

• we illustrated a step of the parser:

• we compared time complexity of this system with other commonly used ones

• implemented by a heap, it can reach <latex>\mathop O(n\log(n))</latex>; however, they don't use it because it's fast enough
• Algorithm1
  • we weren't sure what the variable “index” is (best index of parent or any index of queue of unprocessed words)
  • it again confirms that pseudocode is usually more confusing than a normal code or verbal explanation
• “move” operation:

• Brown et al. clusters - they are surprisingly used rarely

• not much discussed in the paper

• they make the same transformation as they did in Section 2

• 4 optional modules
  • preposition sense disambiguation
  • noun compound interpretation
  • possesives interpretation
  • PropBank semantic role labelling
    • (Hajič et al., 2009) is not in the list of references

• non-directional easy-first parsing
• new features - Brown et al. clusters
• fast and accurate
• modified Penn converter
  • changes in 9.500 POS tags
  • labels copula, coordination
• semantic info