1 / 10

An SVMs Based Multi-lingual Dependency Parsing

An SVMs Based Multi-lingual Dependency Parsing. Yuchang CHENG, Masayuki ASAHARA and Yuji MATSUMOTO Nara Institute of Science and Technology. Approaches to Dependency Parsing. Bottom-up deterministic (local discrimination)

deborah-lancaster
Download Presentation

An SVMs Based Multi-lingual Dependency Parsing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

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 betweenIterative and Shift-reduce methods • Nivre algorithm (Shift-reduce) • depth first • O(n) • Iterative • breadth first • O(n2):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 consulted context Limited right-side contextual info. • A configuration in Nivre method • A configuration in Y&M method I saw a girl with a telescope. saw girl with telescope. I a a

  5. Preliminary comparison • English dependency parsing (Penn Treebank 02-06:training, 23:test) • right context = 2 • right context = 4 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. saw girl with telescope. I a a consulted context Shallow pre-processing + Nivre method • Preprocessing of adjacent words • Then, apply Nivre method • Labels are decided by MaxEnt classifiers I saw a girl with a telescope.

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

More Related