Copyright Mark Johnson, Eugene Charniak, 24th November 2005 --- August 2006
We request acknowledgement in any publications that make use of this software and any code derived from this software. Please report the release date of the software that you are using, as this will enable others to compare their results to yours.
BLLIP Parser is a statistical natural language parser including a
generative constituent parser (first-stage) and discriminative
maximum entropy reranker (second-stage). The latest version can
be found on GitHub. This
document describes basic usage of the command line interface and
describes how to build and run the reranking parser. There are now
Python and Java interfaces
as well. The Python interface is described in README-python.rst.
(optional) For optimal speed, you may want to define
$GCCFLAGSspecifically for your machine. However, this step can be safely skipped as the defaults are usually fine. Withcshortcsh, try something like:shell> setenv GCCFLAGS "-march=pentium4 -mfpmath=sse -msse2 -mmmx"
or:
shell> setenv GCCFLAGS "-march=opteron -m64"
Build the parser with:
shell> make
Sidenote on compiling on OS X
OS X uses the
clangcompiler by default which cannot currently compile the parser. Try setting this environment variable before building to change the default C++ compiler:shell> setenv CXX g++
Recent versions of OS X may have additional issues. See issues 60, 19, and 13 for more information.
The GitHub repository includes parsing and reranker models, though these are mostly around for historical purposes. See this page on BLLIP Parser models for information about obtaining newer and more accurate parsing models.
After it has been built, the parser can be run with:
shell> parse.sh <sourcefile.txt>
For example:
shell> parse.sh sample-text/sample-data.txt
The input text must be pre-sentence segmented with each sentence in an
<s> tag:
<s> Sentence 1 </s> <s> Sentence 2 </s> ...
Note that there needs to be a space before and after the sentence.
The parser distribution currently includes a basic Penn Treebank Wall
Street Journal parsing models which parse.sh will use by default.
The Python interface to the parser includes a mechanism for listing and
downloading additional parsing models (some of which are more accurate,
depending on what you're parsing).
The script parse-and-fuse.sh demonstrates how to run syntactic
parse fusion. Fusion can also be run via the Python bindings.
The script parse-eval.sh takes a list of treebank files as arguments
and extracts the terminal strings from them, runs the two-stage parser
on those terminal strings and then evaluates the parsing accuracy with
Sparseval. For example, if the Penn Treebank 3 is installed at
/usr/local/data/Penn3/, the following code evaluates the two-stage
parser on section 24:
shell> parse-eval.sh /usr/local/data/Penn3/parsed/mrg/wsj/24/wsj*.mrg
The Makefile will attempt to automatically download and build
Sparseval for you if you run make sparseval.
For more information on Sparseval see this paper:
@inproceedings{roark2006sparseval,
title={SParseval: Evaluation metrics for parsing speech},
author={Roark, Brian and Harper, Mary and Charniak, Eugene and
Dorr, Bonnie and Johnson, Mark and Kahn, Jeremy G and
Liu, Yang and Ostendorf, Mari and Hale, John and
Krasnyanskaya, Anna and others},
booktitle={Proceedings of LREC},
year={2006}
}
We no longer distribute evalb with the parser since it sometimes skips sentences unnecessarily. Sparseval does not have these issues.
There is more information about different components of the
parser spread across README files in this distribution (see
below). BLLIP Parser is
maintained by David McClosky.
- Usage help: StackOverflow (use
charniak-parsertag) - Bug reports and feature requests: GitHub issue tracker
- Twitter: @bllipparser
For details on the running the parser, see first-stage/README.rst. For help retraining the parser, see first-stage/TRAIN/README.rst (also includes some information about the parser model file formats).
See second-stage/README
for an overview. second-stage/README-retrain.rst details how to retrain the reranker. The
second-stage/programs/*/README files include additional notes about
different reranker components.
We haven't tested these all of these and can't support them, but they may be useful if you're working on other platforms or languages.
- Native Charniak parser for Windows (doesn't need cygwin, no reranker)
- Rutu Mulkar-Mehta's Windows version
- French branch by Djame Seddah
- Liang Huang's forest reranker (includes forest-dumping extensions)
- Javascript (emscripten) version by Kevin Kwok (live demo, no reranker)
Parser and reranker:
- Eugene Charniak and Mark Johnson. "Coarse-to-fine n-best parsing and MaxEnt discriminative reranking." Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2005.
- Eugene Charniak. "A maximum-entropy-inspired parser." Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. Association for Computational Linguistics, 2000.
Self-training:
- David McClosky, Eugene Charniak, and Mark Johnson. "Effective Self-Training for Parsing." Proceedings of the Conference on Human Language Technology and North American chapter of the Association for Computational Linguistics (HLT-NAACL 2006), 2006.
Syntactic fusion:
- Do Kook Choe, David McClosky, and Eugene Charniak. "Syntactic Parse Fusion." Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), 2015.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent