BLLIP Parser is a statistical natural language parser including a generative constituent parser (first-stage) and discriminative maximum entropy reranker (second-stage). It’s also known as Charniak-Johnson or the Brown reranking parser.
There are parsing and reranker models.
Python and Java interfaces are available.
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).
NLTK provides an interface to the BLLIP reranking parser (aka Charniak-Johnson parser, Charniak parser, Brown reranking parser).
Website: bllip.cs.brown.edu
Support: GitHub Code Repository
Developer: Mark Johnson, Eugene Charniak
License: Apache License 2.0
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