Wide Coverage Incremental Parsing by Learning Attachment Preferences

  • Fabrizio Costa
  • Vincenzo Lombardo
  • Paolo Frasconi
  • Giovanni Soda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2175)


This paper presents a novel method for wide coverage parsing using an incremental strategy, which is psycholinguistically motivated. A recursive neural network is trained on treebank data to learn first pass attachments, and is employed as a heuristic for guidingpa rsingde cision. The parser is lexically blind and uses beam search to explore the space of plausible partial parses and returns the full analysis havinghi ghest probability. Results are based on preliminary tests on the WSJ section of the Penn treebank and suggest that our incremental strategy is a computationally viable approach to parsing.


Parse Tree Sentence Processing Test Sentence Incremental Strategy Connection Path 
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-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Fabrizio Costa
    • 1
  • Vincenzo Lombardo
    • 2
  • Paolo Frasconi
    • 1
  • Giovanni Soda
    • 1
  1. 1.DSIUniversit’ di FirenzeItaly
  2. 2.DiSTAUniversità del Piemonte OrientaleITALY

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