# 1 A Pipeline Model for Bottom-Up Dependency Parsing Ming-Wei Chang, Quang Do, Dan Roth Computer Science Department University of Illinois, Urbana-Champaign.

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1 A Pipeline Model for Bottom-Up Dependency Parsing Ming-Wei Chang, Quang Do, Dan Roth Computer Science Department University of Illinois, Urbana-Champaign Tenth Conference on Natural Language Learning, Shared Task New York, USA, 2006

2 Dependency Parsing Approach: Summary Modified Shift-Reduced parser. Actions are selected via a classifier + Extended action set + Look ahead search Control Policy: Left to right, with step back Dependency Types: A separate multiclass classifier Multilingual: Convert non-projective languages to projective [Nirve and Nilsson, 2005]

3 Motivation Shift-Reduced Parsing as a pipeline model: A classifier is used to determine which action to take The decision at each stage depends on previous decisions. + Making a decision can rely on information acquired in previous stages - Making a decision can rely on incorrect information acquired in previous stages. Viewed this way, we want to: 1. Reduce the number of decisions 2. Make local decisions more robust

4 Parsing from left to right; considering pair of (currently) consecutive words (a,b) (with a { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/10/2719584/slides/slide_4.jpg", "name": "4 Parsing from left to right; considering pair of (currently) consecutive words (a,b) (with a

5 A Pipeline Model with Look Ahead Search Pipilining decisions may result in error accumulation: The correct dependencies  If the algorithm decides w  x before x  y and x  z, we cannot recover the correct parent for y and z. Correct early decisions are crucial A look ahead search algorithm takes into account future predicted actions Local decisions are more robust wxyz wyz x

6 A Pipeline Model with Look Ahead – cont’ The search algorithm performs a search of length depth. Additive scoring is used to score the sequence The first action in this sequence is performed. depth=1 a0a0 a1a1 keep this action depth=2 a0a0 a1a1 a2a2 keep this action depth=3 a0a0 a1a1 a2a2 a3a3 keep this action

7 Experiments (for Swedish) * The effect of the new action. The effect of look ahead search. * For other languages, please refer to our paper.

8 Analysis WaitLeft and the Look Ahead Search improve the parsing results. Results can be improved by: Selecting features and parameters more carefully Currently we use exactly the same set of features and the same parameters for all languages. Using the FEAT column properly The result for languages with FEAT column is generally worse than the languages without FEAT column

9 Thank you !

10 It is not tractable to find the global optimal predicted sequence in the pipeline model with the large depth. In the pipeline framework, the feature vector of current decision depends on every previous predictions. The FEAT column Average difference between our system with the best systems With Feats 4.5%, Without Feats 3.4%

11 Labeling the Dependency Type A post-task after predicting the head for the tokens in the sentence. This is a multi-class classification. Consider every edge of the tree Classify the edge into several classes The parents of the tokens which were labeled in the first phase will be used as the features.

12 a b x ab abc ab L R WL

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16 Experiments We show the effect of the new action (on Swedish).

17 Experiments The effect of look ahead search (on Swedish).

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