  This paper presents the IMS contribution to the PolEval 2018 Shared Task. We
submitted systems for both of the Subtasks of Task 1. In Subtask (A), which was
about dependency parsing, we used our ensemble system from the CoNLL 2017 UD
Shared Task. The system first preprocesses the sentences with a CRF
POS/morphological tagger and predicts supertags with a neural tagger. Then, it
employs multiple instances of three different parsers and merges their outputs
by applying blending. The system achieved the second place out of four
participating teams. In this paper we show which components of the system were
the most responsible for its final performance.
  The goal of Subtask (B) was to predict enhanced graphs. Our approach
consisted of two steps: parsing the sentences with our ensemble system from
Subtask (A), and applying 12 simple rules to obtain the final dependency
graphs. The rules introduce additional enhanced arcs only for tokens with
"conj" heads (conjuncts). They do not predict semantic relations at all. The
system ranked first out of three participating teams. In this paper we show
examples of rules we designed and analyze the relation between the quality of
automatically parsed trees and the accuracy of the enhanced graphs.
