We compare two parsing models for temporal dependency structures, and show that a deterministic non-projective dependency parser outperforms a graph-based maximum spanning tree parser, achieving labeled attachment accuracy of 0.647 and labeled tree edit distance of 0.596.

Comparing the two dependency parsing models, we have found that a shift-reduce parser, which more closely mirrors the incremental processing of our human annotators, outperforms a graph-based maximum spanning tree parser.

Table 2: Features for the shift-reduce parser (SRP) and the graph-based maximum spanning tree (MST) parser.

The Shift-Reduce parser (SRP; Section 4.1) and the graph-based, maximum spanning tree parser (MST; Section 4.2) are compared to these baselines.

In terms of labeled attachment score, both dependency parsing models outperformed the baseline models — the maximum spanning tree parser achieved 0.614 LAS, and the shift-reduce parser achieved 0.647 LAS.

The shift-reduce parser (SRP) trains a machine learning classifier as the oracle 0 E (C —> T) to predict a transition 75 from a parser configuration 0 2 (L1, L2, Q, E), using node features such as the heads of L1, L2 and Q, and edge features from the already predicted temporal relations in E. The graph-based maximum spanning tree (MST) parser trains a machine learning model to predict SCORE(e) for an edge e = (107;, rj, wk), using features of the nodes w, and wk.

The SPANNINGTREE function is usually defined using one of the efficient search techniques for finding a maximum spanning tree.

The result is the globally optimal maximum spanning tree for the graph (Georgiadis, 2003).

The graph-based parsing model aims to search for the maximum spanning tree (MST) in a graph (McDonald et al., 2005).

The parsing model finds a maximum spanning tree (MST), which is the highest scoring tree in T(Gx).

In our approach, we consider the scores of the DLM when searching for the maximum spanning tree.