A Comparison of Chinese Parsers for Stanford Dependencies

Transcription

1 A Comparison of Chinese Parsers for Stanford Dependencies Wanxiang Che, Valentin I. Spitkovsky and Ting Liu Harbin Institute of Technology Stanford University ACL 2012 July 11, 2012 Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

2 Outline Outline 1 Introduction 2 Methodology 3 Results 4 Analysis 5 Conclusion Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

3 Introduction Outline 1 Introduction 2 Methodology 3 Results 4 Analysis 5 Conclusion Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

4 Introduction Stanford Dependencies A simple description of relations between pairs of words in a sentence A kind of semantically-oriented dependency representation Converted from constituent trees by rules 53 binary relations for English, 46 for Chinese Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

5 Introduction Stanford Dependencies A simple description of relations between pairs of words in a sentence A kind of semantically-oriented dependency representation Converted from constituent trees by rules 53 binary relations for English, 46 for Chinese root nsubj dobj det rcmod -Root- I saw the man who loves you ROOT SUB VMOD NMOD nsubj SUB dobj VMOD CLF Figure: Stanford dependencies (above) vs. CoNLL style (below) Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

6 Introduction Stanford Dependencies Applications Intuitive and easy to apply, requires little linguistic expertise Biomedical text mining (Kim et al., 2009) Textual entailment (Androutsopoulos and Malakasiotis, 2010) Information extraction (Wu and Weld, 2010; Banko et al., 2007) Sentiment analysis (Meena and Prabhakar, 2007; Wu et al., 2011) root nsubj dobj det rcmod -Root- I saw the man who loves you ROOT SUB VMOD NMOD nsubj SUB dobj VMOD CLF Figure: Stanford dependencies (above) vs. CoNLL style (below) Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

7 Introduction Parsing Methods Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

8 Introduction Parsing Methods Constituent Parsing (indirect) Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

9 Introduction Parsing Methods Constituent Parsing (indirect) Sentence Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

10 Introduction Parsing Methods Constituent Parsing (indirect) IP NP VP NR VV NP IP 中国 鼓励 ADJP NP VP JJ NN VV NP Sentence 民营企业家投资 NN NN NN 国家基础建设 Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

11 Introduction Parsing Methods Constituent Parsing (indirect) IP NP VP NR VV NP IP 中国鼓励 ADJP NP VP Sentence JJ NN VV NP 民营企业家投资 NN NN NN 国家基础建设 nsubj root dobj dep amod 中国鼓励民营企业家投资国家基础建设 China encourages private entrepreneurs invest national infrastructure construction dobj nn nn Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

12 Introduction Parsing Methods Constituent Parsing (indirect) IP NP VP NR VV NP IP Sentence 中国 鼓励 ADJP JJ 民营 NP NN 企业家 VV 投资 VP NN 国家 NP NN 基础 NN 建设 nsubj root dobj dep amod 中国鼓励民营企业家投资国家基础建设 China encourages private entrepreneurs invest national infrastructure construction Stanford dependency parser s original implementation dobj nn nn Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

13 Introduction Parsing Methods Constituent Parsing (indirect) IP NP VP NR VV NP IP Sentence 中国 鼓励 ADJP JJ 民营 NP NN 企业家 VV 投资 VP NN 国家 NP NN 基础 NN 建设 nsubj root dobj dep amod 中国鼓励民营企业家投资国家基础建设 China encourages private entrepreneurs invest national infrastructure construction Stanford dependency parser s original implementation dobj nn nn Dependency Parsing (direct) Sentence nsubj root dobj dep amod 中国鼓励民营企业家投资国家基础建设 China encourages private entrepreneurs invest national infrastructure construction dobj nn nn Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

14 Introduction Motivation Which method is better for Chinese Stanford Dependencies? Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

15 Introduction Motivation Which method is better for Chinese Stanford Dependencies? Comparison for English (Cer et al., 2010) Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

16 Introduction Motivation Which method is better for Chinese Stanford Dependencies? Comparison for English (Cer et al., 2010) Constituent parsers systematically outperform direct methods Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

17 Introduction Motivation Which method is better for Chinese Stanford Dependencies? Comparison for English (Cer et al., 2010) Constituent parsers systematically outperform direct methods Did not explore more sophisticated (higher-order) dependency parsers Did not explore more consistent (n-way jackknifing of) POS tags Small bug in evaluation of MSTParser Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

18 Methodology Outline 1 Introduction 2 Methodology 3 Results 4 Analysis 5 Conclusion Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

19 Methodology Open Source Parsers Parsers Information Open Source Parsers Type Parser Version Algorithm Constituent Berkeley 1.1 PCFG Bikel 1.2 PCFG Charniak Nov PCFG Stanford 2.0 Factored Dependency MaltParser Arc-Eager Mate 2.0 2nd-order MST MSTParser 0.5 MST Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

20 Methodology Settings Settings Corpus Latest Chinese TreeBank (CTB) 7.0 Number of \in Train Dev Test Total files 2, ,448 sentences 46,572 2,079 2,796 51,447 tokens 1,039,942 59,955 81,578 1,181,475 Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

21 Methodology Settings Settings Corpus Latest Chinese TreeBank (CTB) 7.0 Number of \in Train Dev Test Total files 2, ,448 sentences 46,572 2,079 2,796 51,447 tokens 1,039,942 59,955 81,578 1,181,475 Software and Hardware Parsers: all default options Hardware: Intel s Xeon E GHz CPU and 24GB RAM Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

22 Methodology Features for Dependency Parsers Features for Dependency Parsers POS tags Stanford POS tagger Automatic tags for training data (via 10-way jackknifing) Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

23 Methodology Features for Dependency Parsers Features for Dependency Parsers POS tags Stanford POS tagger Automatic tags for training data (via 10-way jackknifing) Lemmas The last character of each Chinese word E.g., bicycle ( 自行车 ), car ( 汽车 ) and train ( 火车 ) are all various kinds of vehicle ( 车 ) Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

24 Results Outline 1 Introduction 2 Methodology 3 Results 4 Analysis 5 Conclusion Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

25 Results Chinese Results Dev Test Type Parser UAS LAS UAS LAS Time Constituent Berkeley :56 Bikel ,861:31 Charniak :04 Stanford :50 Dependency MaltParser (liblinear) :11 MaltParser (libsvm) :51 Mate (2nd-order) :19 MSTParser (1st-order) :17 Bold: best results. Dark Red: worst results. Blue: best results of constituent parsers. Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

26 Analysis Outline 1 Introduction 2 Methodology 3 Results 4 Analysis 5 Conclusion Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

27 Analysis Comparison between Mate and Berkeley parsers Mate is slightly better than Berkeley (but not significantly, p > 0.05) Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

28 Analysis Comparison between Mate and Berkeley parsers Mate is slightly better than Berkeley (but not significantly, p > 0.05) Performance (F 1 ) comparison on different relations Relation Count Mate Berkeley nn 7, dep 4, nsubj 4, advmod 4, dobj 3, conj 2, prep 2, root 2, nummod 1, assmod 1, Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

29 Analysis More Analysis Feature Effect 10-way jackknifing POS tags for training data Gold Jackknifing Mate Berkeley Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

30 Analysis More Analysis Feature Effect 10-way jackknifing POS tags for training data Gold Jackknifing Mate Berkeley Lemmas for Mate 77.8 (w/o) vs (with) Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

31 Analysis More Analysis Feature Effect 10-way jackknifing POS tags for training data Gold Jackknifing Mate Berkeley Lemmas for Mate 77.8 (w/o) vs (with) English vs. Chinese Chinese English Berkeley Charniak CJ (Charniak + Reranking) 89.1 Mate Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

32 Conclusion Outline 1 Introduction 2 Methodology 3 Results 4 Analysis 5 Conclusion Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

33 Conclusion Conclusion For Chinese, direct approach comparable to using constituents Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

34 Conclusion Conclusion For Chinese, direct approach comparable to using constituents Which parser to use in practice? Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

35 Conclusion Conclusion For Chinese, direct approach comparable to using constituents Which parser to use in practice? Most accurate: Mate parser Fastest: MaltParser (liblinear) Trade-off: Berkeley parser Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

36 Conclusion Conclusion For Chinese, direct approach comparable to using constituents Which parser to use in practice? Most accurate: Mate parser Fastest: MaltParser (liblinear) Trade-off: Berkeley parser We prefer dependency parsers which more easily admit richer features Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

37 Conclusion Conclusion For Chinese, direct approach comparable to using constituents Which parser to use in practice? Most accurate: Mate parser Fastest: MaltParser (liblinear) Trade-off: Berkeley parser We prefer dependency parsers which more easily admit richer features n-way jackknifing of POS tags and lemma features can help Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19

38 Conclusion Thanks and QA Che, Spitkovsky, and Liu (HIT, Stanford) Comparison of Chinese Parsers July 11, / 19