4 research outputs found
GF-DOP: grammatical feature data-oriented parsing
This paper proposes an extension of Tree-DOP which approximates the LFG-DOP model. GF-DOP combines the robustness of the DOP model with some of the linguistic competence of LFG. LFG c-structure trees are augmented with LFG functional information, with the aim of (i) generating
more informative parses than Tree-DOP; (ii) improving overall parse ranking by modelling grammatical features; and (iii) avoiding the inconsistent probability models of LFG-DOP. In a number of experiments on the HomeCentre corpus, we report on which (groups of) features most heavily influence parse quality, both positively and negatively
Proceedings of the LFG06 Conference
This paper proposes an extension of Tree-DOP which approximates the LFG-DOP model. GF-DOP combines the robustness of the DOP model with some of the linguistic competence of LFG. LFG c-structure trees are augmented with LFG functional information, with the aim of (i) generating more informative parses than Tree-DOP; (ii) improving overall parse ranking by modelling gram-matical features; and (iii) avoiding the inconsistent probability models of LFG-DOP. In a number of experiments on the HomeCentre corpus, we report on which (groups of) features most heavily influence parse quality, both positively and negatively.