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Using Verb-Noun Patterns to Detect Process Inputs

  • Munshi Asadullah
  • Damien Nouvel
  • Patrick Paroubek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8655)

Abstract

We present the preliminary results of an ongoing work aimed at using morpho-syntactic patterns to extract information from process descriptions in a semi-supervised manner. The experiments have been designed for generic information extraction tasks and evaluated on detecting ingredients from cooking recipes in French using a large gold standard corpus. The proposed method uses bi-lexical dependency oriented syntactic analysis of the text and extracts relevant morpho-syntactic patterns. Those patterns are then used as features for different machine learning methods to acquire the final ingredient list. Furthermore, this approach may easily be adapted to similar tasks since it relies on mining generic morpho-syntactic patterns from the documents automatically. The method itself is language independent, considering language specific parsers being used. The performance of our method on the DEFT 2013 data set is nevertheless satisfactory since it significantly outperforms the best system from the original challenge (0.75 vs 0.66 MAP).

Keywords

Mean Average Precision Computational Linguistics Entity Recognition Lexical Feature Document Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Munshi Asadullah
    • 1
  • Damien Nouvel
    • 1
  • Patrick Paroubek
    • 1
  1. 1.Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur (LIMSI)Orsay cedexFrance

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