Research Goal

Estimation and Disambiguation Tools

• COMET - Conditional Maximum Entropy Estimation from Truncated Data: The COMET estimator and disambiguator performs discriminative estimation of maximum entropy models from partially labeled data. In case of such truncated data, discriminative or conditional criteria are defined with respect to the set of grammar parses consistent with the treebank annotations . That is, the treebank annotations are used to guide the discriminative estimation of an exponential probability model on linguistically fine grained parses. This estimator and disambiguator has been trained and tested in a large scale experiment using the UPenn Wall Street Journal data set.
  References:
  • Stefan Riezler, Tracy H. King, Ronald M. Kaplan, Richard Crouch, John T. Maxwell III, Mark Johnson (2002). Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL'02), Philadephia, PA.
    
  • Richard Crouch, Ronald M. Kaplan, Tracy H. King, Stefan Riezler (2002). A Comparison of Evaluation Metrics for a Broad Coverage Stochastic Parser. In Proceedings of the Workshop on "Parseval and Beyond" at the 3rd International Conference on Language Resources and Evaluation (LREC'02), 2000, Las Palmas, Spain.

• The Pseudo-Likelihood Estimator: This estimator assumes fully labeled data and performs maximum a-posteriori estimation of conditional probabilities p(x|y) of analyses x given their corresponding sentences y. The conditioning of the parse space on the sentences of the training corpus makes both estimation tractable and yields improved performance due to the discriminative approach of the estimation procedure.
  References:
  • Mark Johnson, Stuart Geman, Stephen Canon, Zhiyi Chi, Stefan Riezler (1999). Estimators for Stochastic "Unification-Based" Grammars. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL'99), College Park, MD.
    
  • Mark Johnson and Stefan Riezler (2000). Exploiting Auxiliary Distributions in Stochastic Unification-Based Grammars. In Proceedings of the 1st Meeting of the North American Chapter of the Association for Computational Linguistics (ANLP-NAACL 2000), Seattle, WA.

• LoLIDa - Estimating Log-Linear Models from Incomplete Data: This estimator applies to the case where no training labels, i.e. no corpus of parsed and manually disambiguated sentences is available. It uses the EM algorithm (Dempster et al. (1977)) to maximize posterior marginal probabilities of the corpus of parsed sentences.
  References:
  • Stefan Riezler, Detlef Prescher, Jonas Kuhn, Mark Johnson (2000). Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL'00) , 2000, Hong Kong.