1. 1 A Fast Deterministic Parser for Chinese Mengqiu Wang, Kenji Sagae and Teruko Mitamura Language Technologies Institute School of Computer Science Carnegie Mellon University

2. 2 Outline of the talk Background Deterministic parsing model Classifier and feature selection POS tagging Experiment and results Discussion and future work Conclusion

3. 3 Background Constituency parsing is one of the most fundamental tasks in NLP. State-of-the-art accuracy previously reported in Chinese constituency parsing achieves precision and recall in the lower 80% using automatically generated POS. Most literature in parsing only reports accuracy, efficiency is typically ignored But in reality, parsers are deemed too slow for many NLP applications (e.g. IR, QA, web-based IX)

4. 4 Deterministic Parsing Model Originally developed in [Sagae and Lavie 2005] for English Input Convention in deterministic parsing assumes input sentences (Chinese in our case) are already segmented and POS tagged 1. Main Data Structure A queue, to store input word-POS pairs A stack, holds partial parse trees Trees are lexicalized. We used the same head-finding rules as [Bikel 2004] The Parser performs binary Shift-Reduce actions based on classifier decisions. Example … 1. We perform our own POS tagging based on SVM

5. 5 Deterministic Parsing Model Cont. Input sentence: 布朗 /NR (Brown/Proper Noun) 访问 /VV (Visits/Verb) 上海 /NR (Shanghai/Proper Noun) Initial parser state: Stack: Θ Queue: NR 布朗 VV 访问 NR 上海 (Brown)(Visits)(Shanghai)

6. 6 Deterministic Parsing Model Cont. Classifier output 1: Shift Action Parser State: Stack: Queue: NR 布朗 VV 访问 NR 上海 (Brown) (Visits)(Shanghai)

7. 7 Deterministic Parsing Model Cont. Action 2: Reduce the first item on stack to a NP node, with node (NR 布朗 ) as the head Parser State: Stack: Queue: VV 访问 NR 上海 NR 布朗 NP (NR 布朗 ) (Brown) (Visits)(Shanghai)

8. 8 Deterministic Parsing Model Cont. Action 3: Shift Parser State: Stack: Queue: VV 访问 NR 上海 NR 布朗 NP (NR 布朗 ) (Brown) (Visits) (Shanghai)

9. 9 Deterministic Parsing Model Cont. Action 4: Shift Parser State: Stack: Queue: Θ VV 访问 NR 上海 NR 布朗 NP (NR 布朗 ) (Brown) (Visits)(Shanghai)

10. 10 Deterministic Parsing Model Cont. Action 5: Reduce the top item on stack to a NP node, with node (NR 上海 ) as the head Parser State: Stack: Queue: Θ VV 访问 NR 布朗 NP (NR 布朗 ) NR 上海 NP (NR 上海 ) (Brown) (Visits) (Shanghai)

11. 11 Deterministic Parsing Model Cont. Action 6: Reduce the top two items on stack to a VP node, with node (VV 访问 ) as the head Parser State: Stack: Queue: Θ NR 布朗 NP (NR 布朗 ) VV 访问 NR 上海 NP (NR 上海 ) VP (VV 访问 ) (Brown) (Visits) (Shanghai)

12. 12 Deterministic Parsing Model Cont. Action 7: Reduce the top two items on stack to an IP node, take the head node of the VP subtree as the head -- (VV 访问 ). Parser State: Stack: Queue: Θ NR 布朗 NP (NR 布朗 ) VV 访问 NR 上海 NP (NR 上海 ) VP (VV 访问 ) (Brown) (Visits) (Shanghai)

13. 13 Deterministic Parsing Model Cont. Parsing terminates when queue is empty and stack only contains one item Final parse tree: NP (NR 上海 ) NR 布朗 NP (NR 布朗 ) VV 访问 NR 上海 VP (VV 访问 ) (Brown) (Visits) (Shanghai)

14. 14 Classifiers Classification is the most important part of deterministic parsing. It determines constituency label of each tree node in the final parse tree. We experimented with four different classifiers: SVM classifier -- finds a hyper-plane that gives the maximum soft margin that minimizes the expected risk. Maximum Entropy Classifier -- estimates a set of parameters that would maximize the entropy over distributions that satisfy certain constraints which force the model to best account for the training data. Decision Tree Classifier -- We used C4.5 [Quinlan 1993] Memory-based Learning -- kNN classifier, Lazy learner, short training time

15. 15 Features The features we used are distributionally derived or linguistically motivated. Each feature carries information about the context of a particular parse state. We denote the top item on the stack as S(1), and second item (from the top) on the stack as S(2), and so on. Similarly, we denote the first item on the queue as Q(1), the second as Q(2), and so on.

16. 16 Features Boolean features indicating presence of punctuations, queue emptiness, last parser action, number of words in constituents, headwords and POS, root nonterminal symbol, dependency among tree nodes, tree path information, relative position. Rhythmic features [Sun and Jurafsky 2004].

17. 17 POS tagging In our model, POS tagging is treated as a separate problem and is done prior to parsing. But we care about the performance of the parser in realistic situations with automatically generated POS tags. We implemented a simple 2-pass POS tagging model based on SVM, achieved 92.5% accuracy.

18. 18 Experiments Standard Chinese Treebank data collection Training set: section 1-270 of CTB 2.0 (3484 sentences, 84873 words). Development set: section 301-326 of CTB 2.0 Testing set: section 271-300 of CTB 2.0 Total: 99629 words, about 1/10 of the size of English Penn Treebank. Standard corpus preparation Empty nodes were removed Functional label of nonterminal nodes removed. Eg. NP-Subj -> NP For scoring we used the evalb 1 program. Labeled recall, labeled precision and F1 (harmonic mean) measures are reported. 1. http://nlp.cs.nyu.edu/evalb

19. 19 Results Comparison of classifiers on development set using gold-standard POS classificationParsing Accuracy ModelAccuracyLRLPF1FailTime SVM94.3%86.9%87.9%87.4%03m 19s Maxent92.6%84.1%85.2%84.6%50m 21s DTree192.0%78.8%80.3%79.5%420m 12s DTree2 -81.6%83.6%82.6%300m 18s MBL90.6%74.3%75.2%74.7%216m 11s

20. 20 Classifier Ensemble Using stacked-classifier techniques, we improved the performance on the dev set from 86.9% and 87.9 for LR and LP, to 90.3% and 90.5%. a 3.4% improvement in LR and a 2.6% improvement in LP over the SVM model.

21. 21 Comparison with related work Results on test set using automatically generated POS. <= 40 words<= 100 words LRLPF1POSLRLPF1POS Traditional probabilistic parsers for Chinese Bikel & Chiang 2000 76.8%77.8%77.3%-73.3%74.6%74.0%- Levy & Manning 2003 79.2%78.4%78.8%----- Xiong et al. 2005 78.7%80.1%79.4%----- Bikel’s Thesis 2004 78.0%81.2%79.6%-74.4%78.5%76.4%- Chiang & Bikel 2002 78.8%81.1%79.9%-75.2%78.0%76.6%- Jiang 2004 80.1%82.0%81.1%92.4%---- Sun & Jurafsky 2004 85.5%86.4%85.9%-83.3%82.2%82.7%- Deterministic parser in this work DTree model 70.0%74.6%72.2%92.5%69.2%74.5%71.9%92.2% SVM model 78.1%81.1%79.6%92.5%75.5%78.5%77.0%92.2% Stacked Classifier 79.2%81.1%80.1%92.5%76.7%78.4%77.5%92.2%

22. 22 Comparison with related work cont. Comparison of parsing speed ModelRuntime Bikel54m 6s Levy & Manning8m 12s DTree0m 14s Maxent0m 24s SVM3m 50s

23. 23 Discussion and future work Deterministic parsing framework opens up lots of opportunities for continuous improvement in applying machine learning techniques Eg. Experiment with other classifiers and classifier ensemble techniques. Experiment with degree-2 features for Maxent model, which may give close performance to the SVM model with a faster speed

24. 24 Conclusion We presented a first work on deterministic approach to Chinese constituency parsing. We achieved comparable results to the state-of-the-art in Chinese probabilistic parsing. We demonstrated deterministic parsing is a viable approach to fast and accurate Chinese parsing. Very fast parsing is made possible for applications that are speed-critical with some tradeoff in accuracy. Advances in machine learning techniques can be directly applied to parsing problem, opens up lots of opportunities for further improvement

25. 25 Reference Daniel M. Bikel and David Chiang. 2000. Two statistical parsing models applied to the Chinese Treebank. In Proceedings of the Second Chinese Language Processing Workshop. Daniel M. Bikel. 2004. On the Parameter Space of Generative Lexicalized Statistical Parsing Models. Ph.D. thesis, University of Pennsylvania. David Chiang and Daniel M. Bikel. 2002. Recovering latent information in treebanks. In Proceedings of the 19th International Conference on Computational Linguistics. Michael John Collins. 1999. Head-driven Statistical Models for Natural Langauge Parsing. Ph.D. thesis, University of Pennsylvania. Walter Daelemans, Jakub Zavrel, Ko van der Sloot, and Antal van den Bosch. 2004. Timbl: Tilburgmemory based learner, version 5.1, reference guide. Technical Report 04-02, ILK Research Group, Tilburg University. Pascale Fung, Grace Ngai, Yongsheng Yang, and Benfeng Chen. 2004. A maximum-entropy Chinese parser augmented by transformation-based learning. ACM Transactions on Asian Language Information Processing, 3(2):159–168. Mary Hearne and Andy Way. 2004. Data-oriented parsing and the Penn Chinese Treebank. In Proceedings of the First International Joint Conference on Natural Language Processing. Zhengping Jiang. 2004. Statistical Chinese parsing. Honours thesis, National University of Singapore. Zhang Le, 2004. Maximum Entropy Modeling Toolkit for Python and C++. Reference Manual. Roger Levy and Christopher D. Manning. 2003. Is it harder to parse Chinese, or the Chinese Treebank? In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics. Xiaoqiang Luo. 2003. A maximum entropy Chinese character-based parser. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing. David M. Magerman. 1994. Natural Language Parsing as Statistical Pattern Recognition. Ph.D. thesis, Stanford University. Quinlan,J.R.: C4.5: Programs for Machine Learning Morgan Kauffman, 1993 Kenji Sagae and Alon Lavie. 2005. A classifier-based parser with linear run-time complexity. In Proceedings of the Ninth International Workshop on Parsing Technology. Deyi Xiong, Shuanglong Li, Qun Liu, Shouxun Lin, and Yueliang Qian. 2005. Parsing the Penn Chinese Treebank with semantic knowledge. In International Joint Conference on Natural Language Processing 2005.

26. 26 Thank you! Questions?