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Introduction to BLLIP Parser models

There are several available parsing models for BLLIP Parser. This document is designed to help you determine which one will perform best for your task. Each one of the parsing models discussed includes a pair of Charniak parser and Johnson reranker models designed to work together (this is called a unified parsing model).

Finding parsing models

If you don't already have the Python bllipparser module, run the following in your shell:

shell% pip install --user bllipparser

Or, if you can run sudo:

shell% sudo pip install bllipparser

Once you have bllipparser, you can use the ModelFetcher functionality to list and download parsing models. To list parsing models, run the following in your shell:

shell% python -mbllipparser.ModelFetcher -l
8 known unified parsing models: [uncompressed size]
    Self-trained model on GENIA treebank and approx. 200k sentences
    from PubMed [152MB]
    WSJ portion of OntoNotes [61MB]
    Self-trained model on OntoNotes-WSJ and the Google Web Treebank
    Wall Street Journal corpus from Penn Treebank, version 2
    ("AnyDomain" version) [52MB]
    Self-trained model on PTB2-WSJ and approx. two million sentences
    from Gigaword (deprecated) [473MB]
    Improved self-trained model on PTB WSJ and two million sentences
    from Gigaword [435MB]
    Wall Street Journal corpus from Penn Treebank, version 3 [55MB]
    Wall Street Journal corpus from Penn Treebank, version 2 (AUXified
    version, deprecated) [55MB]

This list may change as new parsing models are added to the list. To download and install WSJ+Gigaword-v2 (as an example), run the following in your shell:

% python -mbllipparser.ModelFetcher -i WSJ+Gigaword-v2

Parsing models

Depending on the text that you'd like to parse, there are different optimal parsing models. Here are the current recommendations:

  • News text: WSJ+Gigaword-v2
  • Web text: SANCL2012-Uniform
  • Biomedical (PubMed) text: GENIA+PubMed
  • WSJ section 23 evaluations to replicate papers: For purely supervised parser or parser/reranker results, use either WSJ-PTB3 (for Penn Treebank WSJ) or OntoNotes-WSJ (for the OntoNotes version of WSJ). Use WSJ+Gigaword to replicate self-training results, though WSJ+Gigaword-v2 performs slightly better.
  • Everything else: In general, it's probably best to use SANCL2012-Uniform or WSJ+Gigaword-v2 depending on how well-formed your text is (SANCL2012-Uniform for more informal web/email text).