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bllipparser 2015.08.18

Python bindings for the BLLIP natural language parser

The BLLIP parser (also known as the Charniak-Johnson parser or Brown Reranking Parser) is described in the paper Charniak and Johnson (Association of Computational Linguistics, 2005). This package provides the BLLIP parser runtime along with a Python interface. Note that it does not come with any parsing models but includes a model downloader. The primary maintenance for the parser takes place at GitHub.

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.


Install bllipparser with pip:

shell% pip install --user bllipparser

or (if you have sudo access):

shell% sudo pip install bllipparser

To fetch a parsing model and start parsing:

>>> from bllipparser import RerankingParser
>>> rrp = RerankingParser.fetch_and_load('WSJ-PTB3', verbose=True)
[downloads, installs, and loads the model]
>>> rrp.simple_parse("It's that easy.")
"(S1 (S (NP (PRP It)) (VP (VBZ 's) (ADJP (RB that) (JJ easy))) (. .)))"

The first time this is called, this will download and install a parsing model trained from Wall Street Journal in ~/.local/share/bllipparser (it will only be loaded on subsequent calls).

For a list of installable parsing models, run:

shell% python -mbllipparser.ModelFetcher -l

See BLLIP Parser models for information about picking the best parsing model for your text.

Basic usage

The main class in bllipparser is the RerankingParser parser class which provides an interface to the first stage parser and the second stage reranker. The easiest way to construct a RerankingParser object is with the fetch_and_load (see above) or from_unified_model_dir class methods. A unified model is a directory that contains two subdirectories: parser/ and reranker/, each with the respective model files:

>>> from bllipparser import RerankingParser
>>> rrp = RerankingParser.from_unified_model_dir('/path/to/model/')

If you only want the most likely parse of a sentence in Penn Treebank format, use the simple_parse() method:

>>> rrp.simple_parse('This is simple.')
'(S1 (S (NP (DT This)) (VP (VBZ is) (ADJP (JJ simple))) (. .)))'

If you want more information about the parse, you’ll want to use the parse() method which returns an NBestList object. The parser produces an n-best list of the n most likely parses of the sentence (default: n=50). Typically you only want the top parse, but the others are available as well:

>>> nbest_list = rrp.parse('This is a sentence.')

To get information about the top parse (note that the ptb_parse property is a Tree object, described in more detail later):

>>> print repr(nbest_list[0])
ScoredParse('(S1 (S (NP (DT This)) (VP (VBZ is) (NP (DT a) (NN sentence))) (. .)))', parser_score=-29.620656470412328, reranker_score=-7.13760513405013)
>>> print nbest_list[0].ptb_parse
(S1 (S (NP (DT This)) (VP (VBZ is) (NP (DT a) (NN sentence))) (. .)))
>>> print nbest_list[0].parser_score
>>> print nbest_list[0].reranker_score
>>> print len(nbest_list)

You can perform syntactic fusion with the fuse() method. This combines the parses in the n-best list into a single Tree (which may be a parse already present in the n-best list or a novel one):

>>> print nbest_list.fuse()
(S1 (S (NP (DT This)) (VP (VBZ is) (NP (DT a) (NN sentence))) (. .)))

If you have the PyStanfordDependencies package, you can parse straight to Stanford Dependencies:

>>> tokens = nbest_list[0].ptb_parse.sd_tokens()
>>> for token in tokens:
...     print token
Token(index=1, form=u'This', cpos=u'DT', pos=u'DT', head=4, deprel=u'nsubj')
Token(index=2, form=u'is', cpos=u'VBZ', pos=u'VBZ', head=4, deprel=u'cop')
Token(index=3, form=u'a', cpos=u'DT', pos=u'DT', head=4, deprel=u'det')
Token(index=4, form=u'sentence', cpos=u'NN', pos=u'NN', head=0, deprel=u'root')
Token(index=5, form=u'.', cpos=u'.', pos=u'.', head=4, deprel=u'punct')

This will attempt to use a default converter but see docs for how to customize dependency conversion (or if you run into Java version issues).

If you have an existing tokenizer, tokenization can also be specified by passing a list of strings:

>>> nbest_list = rrp.parse(['This', 'is', 'a', 'pretokenized', 'sentence', '.'])

If you’d like to disable the reranker (lowers accuracy, so not normally done), set rerank=False:

>>> nbest_list = rrp.parse('Parser only!', rerank=False)

You can also parse text with existing POS tags (these act as soft constraints). In this example, token 0 (‘Time’) should have tag VB and token 1 (‘flies’) should have tag NNS:

>>> rrp.parse_tagged(['Time', 'flies'], possible_tags={0 : 'VB', 1 : 'NNS'})[0]
ScoredParse('(S1 (NP (VB Time) (NNS flies)))', parser_score=-54.05083561918019, reranker_score=-15.079632500107973)

You don’t need to specify a tag for all words: Here, token 0 (‘Time’) should have tag VB and token 1 (‘flies’) is unconstrained:

>>> rrp.parse_tagged(['Time', 'flies'], possible_tags={0 : 'VB'})[0]
ScoredParse('(S1 (S (VP (VB Time) (NP (VBZ flies)))))', parser_score=-54.3497715 5750189, reranker_score=-16.681734375725263)

You can specify multiple tags for each token. When you do this, the tags for a token will be used in decreasing priority. token 0 (‘Time’) should have tag VB, JJ, or NN and token 1 (‘flies’) is unconstrained:

>>> rrp.parse_tagged(['Time', 'flies'], possible_tags={0 : ['VB', 'JJ', 'NN']})[0]
ScoredParse('(S1 (NP (NN Time) (VBZ flies)))', parser_score=-42.9961920777843, reranker_score=-12.57069545767032)

If you have labeled span constraints, you can require that all parses follow them with parse_constrained. The following requires that the parse contain a VP covering left to Falklands:

>>> rrp.parse_constrained('British left waffles on Falklands .'.split(),
...                       constraints={(1, 5) : ['VP']})[0]
ScoredParse('(S1 (S (NP (NNPS British)) (VP (VBD left) (NP (NNS waffles)) (PP (IN on) (NP (NNP Falklands)))) (. .)))', parser_score=-93.73622897543436, reranker_score=-25.60347808581542)

To force British left to be a noun phrase:

>> rrp.parse_constrained('British left waffles on Falklands .'.split(),
...                      constraints={(0, 2): ['NP']})[0]
ScoredParse('(S1 (S (NP (JJ British) (NN left)) (VP (VBZ waffles) (PP (IN on) (NP (NNP Falklands)))) (. .)))', parser_score=-89.59447837562135, reranker_score=-25.480236524298025)

There are many parser options which can be adjusted (though the defaults should work well for most cases) with set_parser_options. This will change the size of the n-best list and pick the defaults for all other options. It returns a dictionary of the current options:

>>> rrp.set_parser_options(nbest=10)
{'language': 'En', 'case_insensitive': False, 'debug': 0, 'small_corpus': True, 'overparsing': 21, 'smooth_pos': 0, 'nbest': 10}
>>> nbest_list = rrp.parse('The list is smaller now.', rerank=False)
>>> len(nbest_list)

The parser can also be used as a tagger:

>>> rrp.tag("Time flies while you're having fun.")
[('Time', 'NNP'), ('flies', 'VBZ'), ('while', 'IN'), ('you', 'PRP'), ("'re", 'VBP'), ('having', 'VBG'), ('fun', 'NN'), ('.', '.')]

Use this if all you want is a Penn Treebank-style tokenizer:

>>> from bllipparser import tokenize
>>> tokenize("Tokenize this sentence, please.")
['Tokenize', 'this', 'sentence', ',', 'please', '.']

Parsing shell

BLLIP Parser includes an interactive shell for visualizing parses:

shell% python -mbllipparser model

Model can be a unified parsing model or first-stage parsing model on disk or the name of a model known by ModelFetcher, in which case it will be downloaded and installed if it hasn’t been already. If no model is specified, it will list installable parsing models.

Once in the shell, type a sentence to have the parser parse it:

bllip> I saw the astronomer with the telescope.
Tokens: I saw the astronomer with the telescope .

Parser's parse:
(S1 (S (NP (PRP I))
     (VP (VBD saw)
      (NP (NP (DT the) (NN astronomer))
       (PP (IN with) (NP (DT the) (NN telescope)))))
     (. .)))

Reranker's parse: (parser index 2)
(S1 (S (NP (PRP I))
     (VP (VBD saw)
      (NP (DT the) (NN astronomer))
      (PP (IN with) (NP (DT the) (NN telescope))))
     (. .)))

If you have nltk installed, you can use its tree visualization to see the output:

bllip> visual Show me this parse.
Tokens: Show me this parse .

[graphical display of the parse appears]

If you have PyStanfordDependencies installed, you can parse straight to Stanford Dependencies:

bllip> sdparse Now with Stanford Dependencies integration!
Tokens: Now with Stanford Dependencies integration !

Parser and reranker:
 Now [root]
  +-- with [prep]
  |  +-- integration [pobj]
  |     +-- Stanford [nn]
  |     +-- Dependencies [nn]
  +-- ! [punct]

The asciitree package is required to visualize Stanford Dependencies as a tree. If it is not available, the dependencies will be shown in CoNLL-X format.

There is more detailed help inside the shell under the help command.

The Tree class

The parser provides a simple Tree class which provides information about Penn Treebank-style trees:

>>> tree = bllipparser.Tree('(S1 (S (NP (DT This)) (VP (VBZ is) (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree))) (. .)))')
>>> print tree
(S1 (S (NP (DT This)) (VP (VBZ is) (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree))) (. .)))

pretty_string() provides a line-wrapped stringification:

>>> print tree.pretty_string()
(S1 (S (NP (DT This))
     (VP (VBZ is)
      (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree)))
     (. .)))

You can obtain the tokens and tags of the tree:

>>> print tree.tokens()
('This', 'is', 'a', 'fairly', 'simple', 'parse', 'tree', '.')
>>> print tree.tags()
('DT', 'VBZ', 'DT', 'RB', 'JJ', 'NN', 'NN', '.')
>>> print tree.tokens_and_tags()
[('This', 'DT'), ('is', 'VBZ'), ('a', 'DT'), ('fairly', 'RB'), ('simple', 'JJ'), ('parse', 'NN'), ('tree', 'NN'), ('.', '.')]

Or get information about the labeled spans in the tree:

>>> print tree.span()
(0, 8)
>>> print tree.label

You can navigate within the trees and more:

>>> tree.subtrees()
[Tree('(S (NP (DT This)) (VP (VBZ is) (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree))) (. .))')]
>>> tree[0] # first subtree
Tree('(S (NP (DT This)) (VP (VBZ is) (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree))) (. .))')
>>> tree[0].label
>>> tree[0][0] # first subtree of first subtree
Tree('(NP (DT This))')
>>> tree[0][0].label
>>> tree[0][0].span() # [start, end) indices for the span
(0, 1)
>>> tree[0][0].tags() # tags within this span
>>> tree[0][0].tokens() # tuple of all tokens in this span
>>> tree[0][0][0]
Tree('(DT This)')
>>> tree[0][0][0].token
>>> tree[0][0][0].label
>>> tree[0][0][0].is_preterminal()
>>> len(tree[0]) # number of subtrees
>>> for subtree in tree[0]: # iteration works
...    print subtree
(NP (DT This))
(VP (VBZ is) (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree)))
(. .)
>>> for subtree in tree.all_subtrees(): # all subtrees (recursive)
...     print subtree.is_preterminal(), subtree
False (S1 (S (NP (DT This)) (VP (VBZ is) (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree))) (. .)))
False (S (NP (DT This)) (VP (VBZ is) (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree))) (. .))
False (NP (DT This))
True (DT This)
False (VP (VBZ is) (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree)))
True (VBZ is)
False (NP (DT a) (ADJP (RB fairly) (JJ simple)) (NN parse) (NN tree))
True (DT a)
False (ADJP (RB fairly) (JJ simple))
True (RB fairly)
True (JJ simple)
True (NN parse)
True (NN tree)
True (. .)

More examples and advanced features

See the documentation and the examples directory in the repository.


Parser and reranker:

Self-trained parsing models:

Syntactic fusion:

Release highlights

  • 2015.08.18: New APIs for easier use, integrated ModelFetcher with ParsingShell, automatically organize models
  • 2015.08.15: Add syntactic fusion, sigeval, and new self-trained model
  • 2015.07.23: Fix build error, other build system improvements
  • 2015.07.08: Constrained parsing, reranker can now be built with optimization (30% faster), other API additions
  • 2015.01.11: Improved PyStanfordDependencies support, memory leak fixed, API additions, bugfixes
  • 2014.08.29: Add Tree class, RerankerFeatureCorpus module, other API updates
  • 2014.02.09: Add ModelFetcher, RerankingParser improvements
  • 2013.10.16: distutils support, initial PyPI release
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