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An SVMs Based Multi-lingual Dependency Parsing Yuchang CHENG, Masayuki ASAHARA and Yuji MATSUMOTO Nara Institute of Science and Technology.

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Presentation on theme: "An SVMs Based Multi-lingual Dependency Parsing Yuchang CHENG, Masayuki ASAHARA and Yuji MATSUMOTO Nara Institute of Science and Technology."— Presentation transcript:

1 An SVMs Based Multi-lingual Dependency Parsing Yuchang CHENG, Masayuki ASAHARA and Yuji MATSUMOTO Nara Institute of Science and Technology

2 Approaches to Dependency Parsing Bottom-up deterministic (local discrimination) –Iterative, projective [Kudo & Matsumoto 02][Yamada & Matsumoto 03][Cheng, Asahara, Matsumoto 04] –Shift-reduce, projective [Nivre, Scholz 04] –Shift-reduce, pseudo-projective [Nivre, Nilsson 05] N-best + Large margin discrimination (global discrimination) –Projective [McDonald, Crammer, Pereira 05] –Non-projective[McDonald, Pereira, Ribarow, Hajic 05]

3 Comparison between Iterative and Shift-reduce methods Nivre algorithm (Shift-reduce) –depth first –O(n) Iterative –breadth first –O(n 2 ):worst case, empirically near linear + efficient － limited look-ahead Training and parsing are done in the same process ⇒ Number of training instances = Number of parsing steps

4 consulted context Limited right-side contextual info. saw girl with telescope. I saw a girl with a telescope. I a a A configuration in Nivre method A configuration in Y&M method

5 Preliminary comparison English dependency parsing (Penn Treebank 02-06:training, 23:test) –right context = 2 –right context = 4 IterativeNivre Dep. Acc.0.8720.864 Root Acc.0.8600.795 IterativeNivre Dep. Acc.0.8760.866 Root Acc.0.8680.810 Chinese case: Almost no difference/ a little better result in Nivre method

6 Common Disadvantage Local discrimination Single model throughout whole sentence –local (near leaves) and long-distance (near top) parsing should be different models Distinct model at the lowest level –dependency between adjacent words –implemented as a pre-processing

7 consulted context Shallow pre-processing + Nivre method I saw a girl with a telescope. saw girl with telescope. I a a Preprocessing of adjacent words Then, apply Nivre method Labels are decided by MaxEnt classifiers

8 Language: with preprocessingwithout preprocessing LAS:UAS:LAcc.LAS:UAS:LAcc. Arabic 65.1977.7479.0264.9776.7478.56 Chinese 84.2789.4686.4284.3889.5686.52 Czech 76.2483.483.5275.9982.8883.11 Danish 81.7288.6486.1181.3488.4585.81 Dutch 71.7775.4975.8371.1774.9774.99 German 84.1187.6690.6783.7787.5390.5 Japanese 89.9193.1292.489.7992.8592.14 Portugese 85.0790.388.084.5589.6687.1 Slovene 71.4281.1480.9670.5880.0680.62 Spanish 80.4685.1588.980.0984.7188.7 Swedish 81.0888.5783.9981.1688.7184.12 Turkish 61.2274.4973.9161.2674.5973.71 AV: 77.784.684.177.4284.2283.82 Bulgarian 86.3491.389.2786.0291.1889.07

9

10 Speed-up of Kernel SVM Fast methods for kernel-based text analysis [Kudo & Matsumoto 04] Training with 3 rd degree polynomial Kernel Mining of feature combinations in positive/negative support vectors Linearization with obtained feature combinations (20-200 times speed up)

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