An Attempt at Unsupervised Learning of Hierarchical ...

An Attempt at Unsupervised Learning of Hierarchical ...

An Attempt at Unsupervised Learning of Hierarchical Dependency Parsing via the Dependency Model with Valence (DMV) Motivation Dependency Parsing: Search Query Refinement Statistical Machine Translation Unsupervised Learning:

Availability of Large Quantities of Data DMV Pick a Direction (left or right) Generate the first child, or stop; Generate more children, until stop.

Repeat in the other direction. Recurse Porder Pstop Pattach EM Inside-Outside Algorithm: Inside: Pi(i,X,j) = P(X derives ij) Outside: Po(i,X,j) = P(S derives 0iXjl) Re-Estimation:

Frequency of sub-tree (i,X,j)=Pi(i,X,j)*Po(i,X,j) Evaluation Head-percolation of Penn Treebank parses; % edges correct (directed or undirected) in the best (P)CFG parse

Zero Knowledge: 14.4 (29.9) Adjacent Word Heuristic: 33.6 Klein & Manning: 43.2 (63.7) Oracle: 75.5 (77.5) - Pattach: 60.0 (63.3) - Pstop: 53.9 (57.7) - PstopA: 50.0 (54.8) - PstopN: 12.5 (30.8) EM Didnt work out always made things worse,

even when initialized with very good solutions. If started using Zero Knowledge, then after 1 iteration already gets 18.4 (38.4), then worsens. If started using an Ad-Hoc Harmonic for Pattach, then 21.5 (47.1) after 1 iteration, then worse, and similarly even for the Oracle solution Summary: - DMV useful, simple, extensible model; - EM more thorough debugging needed.

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