def spell_score(misspelling, candidates, method=1):
"""
Calculates the edit distance between a misspelling and each candidate according to the chosen method
:param misspelling: misspelling
:param candidates: list of candidates
:param method: chosen method from [1, 2, 3, 4]
:return: list of edit distances between misspelling and each candidate
"""
lexical_scores = [damerau_levenshtein_distance(misspelling, candidate)
for candidate in candidates]
if method == 1:
return lexical_scores
else:
phonetic_scores = [damerau_levenshtein_distance(dm(misspelling)[0], dm(candidate)[0])
for candidate in candidates]
if method == 2:
return [phonetic_score if phonetic_score != 0 else 1 for phonetic_score in phonetic_scores]
elif method == 3:
return [0.5 * (a + b) for a, b in zip(lexical_scores, phonetic_scores)]
elif method == 4:
return [(2 * a + b) ** 2 for a, b in zip(lexical_scores, phonetic_scores)]
else:
raise ValueError('Method must be element from [1, 2, 3, 4]')
def homepage():
value1 = str(request.form['input1'])
value2 = str(request.form['input2'])
fuzzyNoDM = fuzz.ratio(value1, value2)
fuzzyDM = fuzz.ratio(dm(value1)[0], dm(value2)[1])
ptr = homophone(value1, value2)
print(ptr)
redirect('/results')
return correct(value1, value2, ptr, fuzzyDM, fuzzyNoDM)
def candidates(misspellings, language='en'):
vocab = json.load(open("lexicon_" + language + ".json", 'r'))
vocab_dict = load_vocab(vocab)
print(str(len(misspellings)) + ' misspellings to generate candidates for')
candidates_list = []
print("Generating Damerau-Levenshtein candidates")
for i, misspelling in enumerate(misspellings):
candidates_list.append(
levenshtein_candidates(misspelling, vocab_dict, editdistance=2))
print("Generating Double Metaphone candidates edit distance 1")
metaphone_dict = load_metaphones(vocab)
vocab_dict = load_vocab(list(metaphone_dict.keys()))
metaphone_candidates = [
levenshtein_candidates(dm(misspelling)[0], vocab_dict, editdistance=1)
for misspelling in misspellings
]
soundslike_candidates = [
convert_candidates(candidates, detection, metaphone_dict)
for candidates, detection in zip(metaphone_candidates, misspellings)
]
candidates_list = [
list(set(candidates1 + candidates2)) for candidates1, candidates2 in
zip(candidates_list, soundslike_candidates)
]
return candidates_list
def homophone(str1, str2):
print(dm(str1))
print(dm(str2))
if fuzz.ratio(dm(str1)[0], dm(str2)[0]) == 100:
return 1
elif fuzz.ratio(
dm(str1)[1],
dm(str2)[1]) == 100 and dm(str1)[1] != '' and dm(str2)[1] != '':
return 1
def noisychannel_ranking(self, candidates_list):
"""
An approximate implementation of the ranking method described in Lai et al. (2015), 'Automated Misspelling Detection and Correction in Clinical Free-Text Records'
:param candidates_list: list of candidate list per misspelling
:return: list with corrections or k-best corrections
"""
correction_list = []
confidences = []
for misspelling, candidates in zip(self.misspellings, candidates_list):
if not candidates:
correction_list.append('')
continue
score_list = []
for candidate in candidates:
orthographic_edit_distance = damerau_levenshtein_distance(
misspelling, candidate)
phonetic_edit_distance = damerau_levenshtein_distance(
dm(misspelling)[0],
dm(candidate)[0])
spell_score = (2 * orthographic_edit_distance +
phonetic_edit_distance)**2 # P(m|c)
try:
frequency = self.frequency_dict[candidate]
except KeyError:
frequency = 1
frequency_score = 1 / (1 + log(frequency)) # P(c)
score = spell_score * frequency_score # P(c|m) = P(m|c)*P(c)
score_list.append(score)
score_list = np.array(score_list)
if self.k == 1:
try:
correction_list.append(candidates[np.argmin(score_list)])
except ValueError:
correction_list.append('')
else:
correction_list.append(
[candidates[i] for i in np.argsort(score_list)[:self.k]])
return correction_list
def noisychannel_ranking(self, detection_list, candidates_list):
"""
An approximate implementation of the ranking method described in (Lai et al. 2015)
:param detection_list: list of misspellings
:param candidates_list: list of candidate list per misspelling
:param frequency_dict: corpus frequencies from training data
:param k_best: if True, return k highest ranked candidates instead of single one
:return: list with corrections or k-best corrections
"""
correction_list = []
confidences = []
for misspelling, candidates in zip(detection_list, candidates_list):
score_list = []
for candidate in candidates:
orthographic_edit_distance = damerau_levenshtein_distance(misspelling, candidate)
phonetic_edit_distance = damerau_levenshtein_distance(dm(misspelling)[0], dm(candidate)[0])
spell_score = (2 * orthographic_edit_distance + phonetic_edit_distance) ** 2 # P(m|c)
try:
frequency = self.frequency_dict[candidate]
except KeyError:
frequency = 1
frequency_score = 1 / (1 + log(frequency)) # P(c)
score = spell_score * frequency_score # P(c|m) = P(m|c)*P(c)
score_list.append(score)
score_list = np.array(score_list)
if len(score_list) > 1:
sorted_distances = [score_list[i] for i in np.argsort(score_list)]
top1 = sorted_distances[0]
top2 = sorted_distances[1]
confidence = abs(top1 - top2) / top1
confidences.append(confidence)
else:
confidences.append(0)
if self.k == 1:
try:
correction_list.append(candidates[np.argmin(score_list)])
except ValueError:
correction_list.append('')
elif self.k > 1:
correction_list.append([candidates[i] for i in np.argsort(score_list)[:self.k]])
else:
raise ValueError('k must be positive natural number')
self.confidences = confidences
return correction_list
def load_metaphones(vocab):
"""
:param vocab_file: either a list containing the vocabulary, or a text file which contains one lexical item per line
:return: dictionary with mappings between Double Metaphone representations and corresponding lexical items
"""
# MAKE METAPHONE-LEXICAL MAPPING
metaphone_dict = {}
for item in vocab:
metaphones = dm(item)
for metaphone in metaphones:
if metaphone:
try:
metaphone_dict[metaphone].append(item)
except KeyError:
metaphone_dict[metaphone] = []
metaphone_dict[metaphone].append(item)
return metaphone_dict
candidates_list.append(
levenshtein_candidates(misspelling, vocab_dict,
editdistance=1))
else:
print("Generating Damerau-Levenshtein candidates edit distance 2")
for i, misspelling in enumerate(detection_list):
print(i)
candidates_list.append(
levenshtein_candidates(misspelling, vocab_dict,
editdistance=2))
if sys.argv[2] == "all":
print("Generating Double Metaphone candidates edit distance 1")
metaphone_dict = load_metaphones(vocab)
vocab_dict = load_vocab(list(metaphone_dict.keys()))
metaphone_candidates = [
levenshtein_candidates(dm(misspelling)[0],
vocab_dict,
editdistance=1)
for misspelling in detection_list
]
soundslike_candidates = [
convert_candidates(candidates, detection, metaphone_dict)
for candidates, detection in zip(metaphone_candidates,
detection_list)
]
candidates_list = [
list(set(candidates1 + candidates2)) for candidates1,
candidates2 in zip(candidates_list, soundslike_candidates)
]
with open(sys.argv[3], 'w') as f: