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Improved Inference for Unlexicalized Parsing Slav Petrov and Dan Klein

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Unlexicalized Parsing Hierarchical, adaptive refinement: 1,140 Nonterminal symbols1621min Parsing time 531,200 Rewrites [Petrov et al. ‘06] 91.2 F1 score on Dev Set (1600 sentences) DT 1 DT 2 DT 3 DT 4 DT 5 DT 6 DT 7 DT 8 DT 1 DT 2 DT 3 DT 4 DT 1 DT DT 2

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1621 min

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Coarse-to-Fine Parsing [Goodman ‘97, Charniak&Johnson ‘05] Coarse grammar NP … VP NP-dog NP-cat NP-apple VP-run NP-eat… Refined grammar … Treebank Parse Prune NP-17 NP-12 NP-1 VP-6 VP-31… Refined grammar … Parse

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Prune? For each chart item X[i,j], compute posterior probability: …QPNPVP… coarse: refined: E.g. consider the span 5 to 12: < threshold

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1621 min 111 min (no search error)

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[Charniak et al. ‘06] NP … VP NP-dog NP-cat NP-apple VP-run NP-eat… Refined grammar … X A,B,.. Multilevel Coarse-to-Fine Parsing Add more rounds of pre-parsing Grammars coarser than X-bar ??? ?

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Hierarchical Pruning Consider again the span 5 to 12: …QPNPVP… coarse: split in two: …QP1QP2NP1NP2VP1VP2… …QP1 QP3QP4NP1NP2NP3NP4VP1VP2VP3VP4… split in four: split in eight: ……………………………………………

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Intermediate Grammars X-Bar= G 0 G= G1G2G3G4G5G6G1G2G3G4G5G6 Learning DT 1 DT 2 DT 3 DT 4 DT 5 DT 6 DT 7 DT 8 DT 1 DT 2 DT 3 DT 4 DT 1 DT DT 2

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1621 min 111 min 35 min (no search error)

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State Drift (DT tag) some this That these Thatthissome the these thissome that Thatthissome the these thissome that …………………………………………some thesethisThatThisthat EM

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G1G2G3G4G5G6G1G2G3G4G5G6 Learning G1G2G3G4G5G6G1G2G3G4G5G6 Projected Grammars X-Bar= G 0 G= Projection i 0(G)1(G)2(G)3(G)4(G)5(G)0(G)1(G)2(G)3(G)4(G)5(G) G

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Estimating Projected Grammars Nonterminals? Nonterminals in G NP 1 VP 1 VP 0 S0S0 S1S1 NP 0 Nonterminals in (G) VP S NP Projection Easy:

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Rules in G Rules in (G) Estimating Projected Grammars Rules? S 1 NP 1 VP S 1 NP 1 VP S 1 NP 2 VP S 1 NP 2 VP S 2 NP 1 VP S 2 NP 1 VP S 2 NP 2 VP S 2 NP 2 VP S NP VP ? ???

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Treebank Estimating Projected Grammars [Corazza & Satta ‘06] Rules in (G) S NP VP Rules in G S1 NP1 VP S1 NP1 VP S1 NP2 VP S1 NP2 VP S2 NP1 VP S2 NP1 VP S2 NP2 VP S2 NP2 VP Infinite tree distribution … … 0.56 Estimating Grammars

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Calculating Expectations Nonterminals: c k (X) : expected counts up to depth k Converges within 25 iterations (few seconds) Rules:

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1621 min 111 min 35 min 15 min (no search error)

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G1G2G3G4G5G6G1G2G3G4G5G6 Learning Parsing times X-Bar= G 0 G= 60 % 12 % 7 % 6 % 5 % 4 %

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Bracket Posteriors (after G 0 )

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Bracket Posteriors (after G 1 )

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Bracket Posteriors (Movie)(Final Chart)

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Bracket Posteriors (Best Tree)

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Parse Selection Computing most likely unsplit tree is NP-hard: Settle for best derivation. Rerank n-best list. Use alternative objective function. Parses: -2 Derivations:

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Parse Risk Minimization Expected loss according to our beliefs: T T : true tree T P : predicted tree L : loss function (0/1, precision, recall, F1) [Titov & Henderson ‘06] Use n-best candidate list and approximate expectation with samples.

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Reranking Results ObjectivePrecisionRecallF1Exact BEST DERIVATION Viterbi Derivation Exact (non-sampled) Exact/F1 (oracle) RERANKING Precision (sampled) Recall (sampled) F1 (sampled) Exact (sampled)

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Dynamic Programming [Matsuzaki et al. ‘05] Approximate posterior parse distribution à la [Goodman ‘98] Maximize number of expected correct rules

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ObjectivePrecisionRecallF1Exact BEST DERIVATION Viterbi Derivation DYNAMIC PROGRAMMING Variational Max-Rule-Sum Max-Rule-Product Dynamic Programming Results

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Final Results (Efficiency) Berkeley Parser: 15 min 91.2 F-score Implemented in Java Charniak & Johnson ‘05 Parser 19 min 90.7 F-score Implemented in C

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Final Results (Accuracy) ≤ 40 words F1 all F1 ENG Charniak&Johnson ‘05 (generative) This Work Charniak&Johnson ‘05 (reranked) GER Dubey ‘ This Work CHN Chiang et al. ‘ This Work

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Conclusions Hierarchical coarse-to-fine inference Projections Marginalization Multi-lingual unlexicalized parsing

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Thank You! Parser available at

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