Source code for nlpaug.augmenter.word.spelling

    Augmenter that apply spelling error simulation to textual input.

import os

import nlpaug
import nlpaug.model.word_dict as nmwd
from nlpaug.augmenter.word import WordAugmenter
from nlpaug.util import Action, Doc, LibraryUtil


def init_spelling_error_model(dict_path, include_reverse, force_reload=False):
    # Load model once at runtime
    if SPELLING_ERROR_MODEL and not force_reload:

    spelling_error_model = nmwd.Spelling(dict_path, include_reverse)

    SPELLING_ERROR_MODEL = spelling_error_model


[docs]class SpellingAug(WordAugmenter): # """ Augmenter that leverage pre-defined spelling mistake dictionary to simulate spelling mistake. :param str dict_path: Path of misspelling dictionary :param float aug_p: Percentage of word will be augmented. :param int aug_min: Minimum number of word will be augmented. :param int aug_max: Maximum number of word will be augmented. If None is passed, number of augmentation is calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result from aug_p. Otherwise, using aug_max. :param list stopwords: List of words which will be skipped from augment operation. :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation. :param func tokenizer: Customize tokenization process :param func reverse_tokenizer: Customize reverse of tokenization process :param str name: Name of this augmenter >>> import nlpaug.augmenter.word as naw >>> aug = naw.SpellingAug(dict_path='./spelling_en.txt') """ def __init__(self, dict_path=None, name='Spelling_Aug', aug_min=1, aug_max=10, aug_p=0.3, stopwords=None, tokenizer=None, reverse_tokenizer=None, include_reverse=True, stopwords_regex=None, verbose=0): super().__init__( action=Action.SUBSTITUTE, name=name, aug_p=aug_p, aug_min=aug_min, aug_max=aug_max, stopwords=stopwords, tokenizer=tokenizer, reverse_tokenizer=reverse_tokenizer, device='cpu', verbose=verbose, stopwords_regex=stopwords_regex, include_detail=False) self.dict_path = dict_path if dict_path else os.path.join(LibraryUtil.get_res_dir(), 'word', 'spelling', 'spelling_en.txt') self.include_reverse = include_reverse self.model = self.get_model(force_reload=False) def skip_aug(self, token_idxes, tokens): results = [] for token_idx in token_idxes: # Some words do not exit. It will be excluded in lucky draw. token = tokens[token_idx] if token in self.model.dict and len(self.model.dict[token]) > 0: results.append(token_idx) return results def substitute(self, data): if not data or not data.strip(): return data change_seq = 0 doc = Doc(data, self.tokenizer(data)) aug_idxes = self._get_aug_idxes(doc.get_original_tokens()) if aug_idxes is None or len(aug_idxes) == 0: if self.include_detail: return data, [] return data for aug_idx, original_token in enumerate(doc.get_original_tokens()): # Skip if no augment for word if aug_idx not in aug_idxes: continue candidate_words = self.model.predict(original_token) substitute_token = '' if candidate_words: substitute_token = self.sample(candidate_words, 1)[0] else: # Unexpected scenario. Adding original token substitute_token = original_token if aug_idx == 0: substitute_token = self.align_capitalization(original_token, substitute_token) change_seq += 1 doc.add_change_log(aug_idx, new_token=substitute_token, action=Action.SUBSTITUTE, change_seq=self.parent_change_seq + change_seq) if self.include_detail: return self.reverse_tokenizer(doc.get_augmented_tokens()), doc.get_change_logs() else: return self.reverse_tokenizer(doc.get_augmented_tokens()) def get_model(self, force_reload): return init_spelling_error_model(self.dict_path, self.include_reverse, force_reload)