• DocumentCode
    172490
  • Title

    Improving malt dependency parser using a simple grammar-driven unlexicalised dependency parser

  • Author

    Eragani, Anil Krishna ; Kuchibhotla, Varun

  • Author_Institution
    Language Technol. Res. Center, IIIT Hyderabad, Hyderabad, India
  • fYear
    2014
  • fDate
    20-22 Oct. 2014
  • Firstpage
    211
  • Lastpage
    214
  • Abstract
    In this paper, we present an approach to integrate unlexicalised grammatical features into Malt dependency parser. Malt parser is a lexicalised parser, and like every lexicalised parser, it is prone to data sparseness. We aim to address this problem by providing features from an unlexicalised parser. Contrary to lexicalised parsers, unlexicalised parsers are known for their robustness. We build a simple unlexicalised grammatical parser with POS tag sequences as grammar rules. We use the features from the grammatical parser as additional features to Malt. We achieved improvements of about 0.17-0.30% (UAS) on both English and Hindi state-of-the-art Malt results.
  • Keywords
    grammars; natural language processing; English state-of-the-art malt result; Hindi state-of-the-art malt result; POS tag sequences; data sparseness; grammar rules; grammar-driven unlexicalised dependency parser; malt dependency parser; unlexicalised grammatical features; unlexicalised grammatical parser; unlexicalised parsers; Data mining; Feature extraction; Grammar; Indexes; Robustness; Training; Training data; Malt Parser; syntactic parsing; unlexicalised grammar;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Asian Language Processing (IALP), 2014 International Conference on
  • Conference_Location
    Kuching
  • Type

    conf

  • DOI
    10.1109/IALP.2014.6973482
  • Filename
    6973482