Do automatic annotation techniques have any impact on supervised complex question answering?

Yllias Chali, Sadid A. Hasan, Shafiq R. Joty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

In this paper, we analyze the impact of different automatic annotation methods on the performance of supervised approaches to the complex question answering problem (defined in the DUC-2007 main task). Huge amount of annotated or labeled data is a prerequisite for supervised training. The task of labeling can be accomplished either by humans or by computer programs. When humans are employed, the whole process becomes time consuming and expensive. So, in order to produce a large set of labeled data we prefer the automatic annotation strategy. We apply five different automatic annotation techniques to produce labeled data using ROUGE similarity measure, Basic Element (BE) overlap, syntactic similarity measure, semantic similarity measure, and Extended String Subsequence Kernel (ESSK). The representative supervised methods we use are Support Vector Machines (SVM), Conditional Random Fields (CRF), Hidden Markov Models (HMM), and Maximum Entropy (Max-Ent). Evaluation results are presented to show the impact.

Original languageEnglish
Title of host publicationACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.
Pages329-332
Number of pages4
Publication statusPublished - 1 Dec 2009
EventJoint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009 - Suntec, Singapore
Duration: 2 Aug 20097 Aug 2009

Publication series

NameACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.

Other

OtherJoint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009
CountrySingapore
CitySuntec
Period2/8/097/8/09

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Chali, Y., Hasan, S. A., & Joty, S. R. (2009). Do automatic annotation techniques have any impact on supervised complex question answering? In ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf. (pp. 329-332). (ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.).