nltk.tokenize.repp module

class nltk.tokenize.repp.ReppTokenizer[source]

Bases: nltk.tokenize.api.TokenizerI

A class for word tokenization using the REPP parser described in Rebecca Dridan and Stephan Oepen (2012) Tokenization: Returning to a Long Solved Problem - A Survey, Contrastive Experiment, Recommendations, and Toolkit. In ACL. http://anthology.aclweb.org/P/P12/P12-2.pdf#page=406

>>> sents = ['Tokenization is widely regarded as a solved problem due to the high accuracy that rulebased tokenizers achieve.' ,
... 'But rule-based tokenizers are hard to maintain and their rules language specific.' ,
... 'We evaluated our method on three languages and obtained error rates of 0.27% (English), 0.35% (Dutch) and 0.76% (Italian) for our best models.'
... ]
>>> tokenizer = ReppTokenizer('/home/alvas/repp/') 
>>> for sent in sents:                             
...     tokenizer.tokenize(sent)                   
...
(u'Tokenization', u'is', u'widely', u'regarded', u'as', u'a', u'solved', u'problem', u'due', u'to', u'the', u'high', u'accuracy', u'that', u'rulebased', u'tokenizers', u'achieve', u'.')
(u'But', u'rule-based', u'tokenizers', u'are', u'hard', u'to', u'maintain', u'and', u'their', u'rules', u'language', u'specific', u'.')
(u'We', u'evaluated', u'our', u'method', u'on', u'three', u'languages', u'and', u'obtained', u'error', u'rates', u'of', u'0.27', u'%', u'(', u'English', u')', u',', u'0.35', u'%', u'(', u'Dutch', u')', u'and', u'0.76', u'%', u'(', u'Italian', u')', u'for', u'our', u'best', u'models', u'.')
>>> for sent in tokenizer.tokenize_sents(sents): 
...     print(sent)                              
...
(u'Tokenization', u'is', u'widely', u'regarded', u'as', u'a', u'solved', u'problem', u'due', u'to', u'the', u'high', u'accuracy', u'that', u'rulebased', u'tokenizers', u'achieve', u'.')
(u'But', u'rule-based', u'tokenizers', u'are', u'hard', u'to', u'maintain', u'and', u'their', u'rules', u'language', u'specific', u'.')
(u'We', u'evaluated', u'our', u'method', u'on', u'three', u'languages', u'and', u'obtained', u'error', u'rates', u'of', u'0.27', u'%', u'(', u'English', u')', u',', u'0.35', u'%', u'(', u'Dutch', u')', u'and', u'0.76', u'%', u'(', u'Italian', u')', u'for', u'our', u'best', u'models', u'.')
>>> for sent in tokenizer.tokenize_sents(sents, keep_token_positions=True): 
...     print(sent)                                                         
...
[(u'Tokenization', 0, 12), (u'is', 13, 15), (u'widely', 16, 22), (u'regarded', 23, 31), (u'as', 32, 34), (u'a', 35, 36), (u'solved', 37, 43), (u'problem', 44, 51), (u'due', 52, 55), (u'to', 56, 58), (u'the', 59, 62), (u'high', 63, 67), (u'accuracy', 68, 76), (u'that', 77, 81), (u'rulebased', 82, 91), (u'tokenizers', 92, 102), (u'achieve', 103, 110), (u'.', 110, 111)]
[(u'But', 0, 3), (u'rule-based', 4, 14), (u'tokenizers', 15, 25), (u'are', 26, 29), (u'hard', 30, 34), (u'to', 35, 37), (u'maintain', 38, 46), (u'and', 47, 50), (u'their', 51, 56), (u'rules', 57, 62), (u'language', 63, 71), (u'specific', 72, 80), (u'.', 80, 81)]
[(u'We', 0, 2), (u'evaluated', 3, 12), (u'our', 13, 16), (u'method', 17, 23), (u'on', 24, 26), (u'three', 27, 32), (u'languages', 33, 42), (u'and', 43, 46), (u'obtained', 47, 55), (u'error', 56, 61), (u'rates', 62, 67), (u'of', 68, 70), (u'0.27', 71, 75), (u'%', 75, 76), (u'(', 77, 78), (u'English', 78, 85), (u')', 85, 86), (u',', 86, 87), (u'0.35', 88, 92), (u'%', 92, 93), (u'(', 94, 95), (u'Dutch', 95, 100), (u')', 100, 101), (u'and', 102, 105), (u'0.76', 106, 110), (u'%', 110, 111), (u'(', 112, 113), (u'Italian', 113, 120), (u')', 120, 121), (u'for', 122, 125), (u'our', 126, 129), (u'best', 130, 134), (u'models', 135, 141), (u'.', 141, 142)]
__init__(repp_dir, encoding='utf8')[source]
tokenize(sentence)[source]

Use Repp to tokenize a single sentence.

Parameters

sentence (str) – A single sentence string.

Returns

A tuple of tokens.

Return type

tuple(str)

tokenize_sents(sentences, keep_token_positions=False)[source]

Tokenize multiple sentences using Repp.

Parameters

sentences (list(str)) – A list of sentence strings.

Returns

A list of tuples of tokens

Return type

iter(tuple(str))

generate_repp_command(inputfilename)[source]

This module generates the REPP command to be used at the terminal.

Parameters

inputfilename (str) – path to the input file

static parse_repp_outputs(repp_output)[source]

This module parses the tri-tuple format that REPP outputs using the “–format triple” option and returns an generator with tuple of string tokens.

Parameters

repp_output (type) –

Returns

an iterable of the tokenized sentences as tuples of strings

Return type

iter(tuple)

find_repptokenizer(repp_dirname)[source]

A module to find REPP tokenizer binary and its repp.set config file.