A Fast Deterministic Parser for Chinese

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A Fast Deterministic Parser for Chinese

• Mengqiu Wang, Kenji Sagae and Teruko Mitamura
• Language Technologies Institute
• School of Computer Science
• Carnegie Mellon University

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Outline of the talk

• Background
• Deterministic parsing model
• Classifier and feature selection
• POS tagging
• Experiment and results
• Discussion and future work
• Conclusion

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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)

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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 tagged1.
• 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
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Deterministic Parsing Model Cont.

• Input sentence
• ??/NR (Brown/Proper Noun) ??/VV (Visits/Verb)
• ??/NR (Shanghai/Proper Noun)
• Initial parser state
• Stack T
• Queue

(Brown)
(Visits)
(Shanghai)
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Deterministic Parsing Model Cont.

• Classifier output 1 Shift Action
• Parser State
• Stack
• Queue

(Brown)
(Visits)
(Shanghai)
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Deterministic Parsing Model Cont.

• Action 2 Reduce the first item on stack to a NP
  node, with node (NR ??) as the head
• Parser State
• Stack
• Queue

(Brown)
(Visits)
(Shanghai)
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Deterministic Parsing Model Cont.

• Action 3 Shift
• Parser State
• Stack
• Queue

(Visits)
(Brown)
(Shanghai)
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Deterministic Parsing Model Cont.

• Action 4 Shift
• Parser State
• Stack
• Queue T

(Visits)
(Shanghai)
(Brown)
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Deterministic Parsing Model Cont.

• Action 5 Reduce the top item on stack to a NP
  node, with node (NR ??) as the head
• Parser State
• Stack
• Queue T

(Visits)
(Brown)
(Shanghai)
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Deterministic Parsing Model Cont.

• Action 6 Reduce the top two items on stack to a
  VP node, with node (VV ??) as the head
• Parser State
• Stack
• Queue T

VP (VV ??)
NP (NR ??)
(Brown)
NR
(Visits)
??
(Shanghai)
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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 ??).
• Parser State
• Stack
• Queue T

VP (VV ??)
VP (VV ??)
NP (NR ??)
(Brown)
NR
(Visits)
??
(Shanghai)
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Deterministic Parsing Model Cont.

• Parsing terminates when queue is empty and stack
  only contains one item
• Final parse tree

VP (VV ??)
VP (VV ??)
NP (NR ??)
(Brown)
NR
(Visits)
??
(Shanghai)
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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

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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.

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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.

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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.

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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 -gt NP
• For scoring we used the evalb1 program. Labeled
  recall, labeled precision and F1 (harmonic mean)
  measures are reported.

1. http//nlp.cs.nyu.edu/evalb
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Results

• Comparison of classifiers on development set
  using gold-standard POS

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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.

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Comparison with related work
Results on test set using automatically generated
POS.
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Comparison with related work cont.

• Comparison of parsing speed

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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

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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

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Reference

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  Language Processing Workshop.
• Daniel M. Bikel. 2004. On the Parameter Space of
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• David Chiang and Daniel M. Bikel. 2002.
  Recovering latent information in treebanks. In
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• Walter Daelemans, Jakub Zavrel, Ko van der Sloot,
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• Quinlan,J.R. C4.5 Programs for Machine Learning
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• Kenji Sagae and Alon Lavie. 2005. A
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Thank you!

• Questions?