A hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus

Part of book or chapter of book English OPEN
Zhou, Deyu ; He, Yulan (2008)
  • Publisher: Association for Computational Linguistics
  • Subject:
    arxiv: Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)

We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM-SVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector machines. By employing a modified K-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. These sentences together with their abstract annotations are used to train an HVS model which could be subsequently applied on the whole corpus to generate semantic parsing results. The most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the HM-SVMs. The proposed framework has been tested on the DARPA Communicator Data. Experimental results show that an improvement over the baseline HVS parser has been observed using the hybrid framework. When compared with the HM-SVMs trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences.
  • References (12)
    12 references, page 1 of 2

    Abou-Moustafa, K.T., C.Y. Suen, and M. Cheriet. 2004. A generative-discriminative hybrid for sequential data classification. In Acoustics, Speech, and Signal Processing, 2004 (ICASSP '04), volume 5, pages 805-808.

    Altun, Y., I. Tsochantaridis, and T. Hofmann. 2003. Hidden markov support vector machines. In International Conference in Machine Learning, pages 3- 10.

    Bouchard, Guillaume and Bill Triggs. 2004. The tradeoff between generative and discriminative classifiers. In Proc. of COMPSTAT 2004, pages 721-728.

    CUData. 2004. Darpa communicator travel data. university of colorado at boulder. Avaiable from http://communicator.colorado.edu/phoenix.

    Dowding, J., R. Moore, F. Andry, and D. Moran. 1994. Interleaving syntax and semantics in an efficient bottom-up parser. In Proc. of the 32th Annual Meeting of the Association for Computational Linguistics, pages 110-116, Las Cruces, New Mexico, USA.

    He, Yulan and Steve Young. 2005. Semantic processing using the hidden vector state model. Computer Speech and Language, 19(1):85-106.

    Holub, Alex D., Max Welling, and Pietro Perona1. 2008. Hybrid generative-discriminative visual categorization. International Journal of Computer Vision, 77:239-258.

    Jaakkola, T. and D. Haussler. 1998. Exploiting generative models in discriminative classifiers. In Proc. of Advances in Neural Information Processing 11.

    Ng, A. and M. Jordan. 2002. On generative vs. discriminative classifiers: A comparison of logistic regression and naive bayes. In Proc. of Advances in Neural Information Processing 15, pages 841-848.

    Tsochantaridis, Ioannis, Thorsten Joachims, Thomas Hofmann, and Yasemin Altun. 2005. Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res., 6:1453-1484.

  • Metrics
    views in OpenAIRE
    views in local repository
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    Aston Publications Explorer - IRUS-UK 0 3
Share - Bookmark