1. Yu-Chieh Wu Yue-Shi Lee Jie-Chi Yang National Central University, Taiwan Ming Chuan University, Taiwan Date: 2006/6/8 Reporter: Yu-Chieh Wu The Exploration of Deterministic and Efficient Dependency Parsing

2. Context Nivre ’ s method is a LINEAR-TIME parsing algorithm But it presumed the projective grammar relation for text –One solution is to applying the psuedo projectivization (Nivre and Nilson, 2005) In addition, non-projective words or roots were still kept in stack –Un-parsed words In multilingual scenario, some languages annotated labels for roots

3. In this paper Extend the time efficiency of the Nivre ’ s method DO NOT scan the word sequence multiple times –Perform the Niver ’ s algorithm –Only focused on the “ UN-PARSED ” words Efficiently label the roots

4. System Overview Nivre’s Parser Learner 1 Root Parser Learner 2 Post- Processor Learner 3 Un-Parsed Text Un-Parsed Text Parsed Text Parsed Text Un- Parsed Words Un- Parsed Words

5. Our solution is… To reduce the un-parsed rate –We performed both Forward parsing Backward parsing directions (usually better) To classify the remaining words in stacks –A root parser to identify the word is… Root (including root label) or not root To re-connect the non-projective words –A post-processor is used to re-construct the arcs Exhaustive from the sentence start –Regardless its children

6. Statistics of un-parsed rate (percentage) Un-Parsed Rate ForwardBackward Arabic10.31.4 Chinese4.012.3 Czech16.15.6 Danish12.82.5 Dutch18.49.8 German12.79.2 ForwardBackward Japanese1.14.4 Portugese24.33.17 Slovene14.95.5 Spanish200.5 Swedish19.12.8 Turkish2.54 Bulgarian15.71.2 AVG13.224.02

7. Root Parser For each un-parsed words Word i-2 Bigram i-2 BiPOS i-2 Word i-1 Bigram i-1 BiPOS i-1 Word i Bigram i BiPOS i Word i+1 Bigram i+1 BiPOS i+1 Word i+2 Bigram i+2 BiPOS i+2 Child R Bigram BiPOS Child 0 Bigram BiPOS

8. Experimental Results A (New result) B (Old result) C (Maltparser) Statistic test A vs BB vs CA vs C Arabic63.7563.8154.11NoYes Chinese81.2574.8173.92YesNoYes Czech71.2459.36 YesNoYes Danish79.5278.3877.31No Dutch68.45 63.61NoYes German79.5776.52 YesNoYes Japanese91.4390.1189.07YesNoYes Portugese81.3381.4775.38NoYes Slovene68.4167.8355.04NoYes Spanish74.6572.9972.81YesNoYes Swedish79.5371.7276.28Yes Turkish55.3355.0952.18NoYes Bulgarian81.2379.73 No AVG74.5372.3269.64

9. Parsing performance of different grained POS tags and forward/backward parsing directions Parsing direction LA-Score POS grained LA-Score JaForward91.43 Forward Fine91.43 Backward85.75Coarse91.25 ArForward60.62 Backward Fine63.55 Backward63.55Coarse63.63 TuForward55.47 Forward Fine55.47 Backward55.59Coarse55.59

10. Conclusion In this paper, we investigate the how effect does the “ fast parser ” achieve The employed features were quite simple –Only C/F-POS tag and word form We extend the Nivre ’ s method –Root parser –Exhaustive post-processing

11. Questions ?

12. System Spec Ⅰ.Ⅰ. Parsing Algorithm: 1. Nivre's Algorithm (Nivre, 2003) 2. Root Parser 3. Exhaustive-based Post-processing Ⅱ.Ⅱ. Parser Characteristics: 1. Top-down + Bottom-up 2. Deterministic + Exhaustive 3. Labeling integrated 4. Projective Ⅲ.Ⅲ. Learner:SVM Light (Joachims, 1998) (1)One-versus-One (2)Linear Kernel Ⅳ.Ⅳ. Feature Set: 1. Lexical (Unigram/Bigram) 2. Fine-grained POS and Coarse grained BiCPOS Ⅴ.Ⅴ. Post-Processing: Another learner is used to re-recognize heads in stacks Ⅵ.Ⅵ. Additional/Extern al Resources: Non-Used