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Python bindings for the BLLIP natural language parser

Project Description

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 code provides a Python interface to the parser. Note that it does not contain any parsing models which must be downloaded separately (for example, WSJ self-trained parsing model). The primary maintenance for the parser takes place at GitHub.

Basic usage

The easiest way to construct a parser is with the load_unified_model_dir class method. A unified model is a directory that contains two subdirectories: parser/ and reranker/, each with the respective model files:

>>> from bllipparser import RerankingParser, tokenize
>>> rrp = RerankingParser.load_unified_model_dir('/path/to/model/')

Parsing a single sentence and reading information about the top parse with parse(). 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.')

Getting information about the top parse:

>>> print repr(nbest_list[0])
ScoredParse('(S1 (S (NP (DT This)) (VP (VBZ is) (NP (DT a) (NN sentence))) (. .)))', parser_score=-29.621201629004183, reranker_score=-7.9273829816098731)
>>> 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)

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', '.'])

The reranker can be disabled by setting rerank=False:

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

Parsing text with existing POS tag (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=-53.94938875760073, reranker_score=-15.841407102717749)

You don’t need to specify a tag for all words: 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=-54.390430751112156, reranker_score=-17.290145080887005)

You can specify multiple tags for each token: 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=-42.82904107213723, reranker_score=-12.865900776775314)

Use this if all you want is a tokenizer:

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

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