def test_latin_stemmer(self):
"""Test Latin stemmer."""
sentence = 'Est interdum praestare mercaturis rem quaerere, nisi tam periculosum sit, et item foenerari, si tam honestum.' # pylint: disable=line-too-long
stemmer = Stemmer()
stemmed_text = stemmer.stem(sentence.lower())
target = 'est interd praestar mercatur r quaerere, nisi tam periculos sit, et it foenerari, si tam honestum. ' # pylint: disable=line-too-long
self.assertEqual(stemmed_text, target)
def test_latin_stemmer(self):
"""Test Latin stemmer."""
sentence = 'Est interdum praestare mercaturis rem quaerere, nisi tam periculosum sit, et item foenerari, si tam honestum.' # pylint: disable=line-too-long
stemmer = Stemmer()
stemmed_text = stemmer.stem(sentence.lower())
target = 'est interd praestar mercatur r quaerere, nisi tam periculos sit, et it foenerari, si tam honestum. ' # pylint: disable=line-too-long
self.assertEqual(stemmed_text, target)
def compare_sentences(self, str_a, str_b, language):
"""Tokenize two input strings on sentence boundary and return a
matrix of Levenshtein distance ratios.
:param language: str (language name)
:param string_a: str
:param string_b: str
:return: list [[Comparison]]
"""
sents_a = []
sents_b = []
ratios = []
# Make the latin tokenizer
if language == "latin":
sent_tokenizer = TokenizeSentence('latin')
# Make the greek tokenizer
elif language == "greek":
sent_tokenizer = TokenizeSentence('greek')
# Otherwise, if language, is unsupported, throw error stating accepted Language
# values that may be used to tokenize sentences
else:
print("Language for sentence tokenization not recognized. "
"Accepted values are 'latin' and 'greek'.")
return
# If class instance is set to stem words, do so
if self.stem_words:
stemmer = Stemmer()
str_a = stemmer.stem(str_a)
str_b = stemmer.stem(str_b)
# Tokenize input strings
sents_a = sent_tokenizer.tokenize_sentences(str_a)
sents_b = sent_tokenizer.tokenize_sentences(str_b)
# Process sentences for comparison (taking into account sanitization settings)
sents_a = self._process_sentences(sents_a)
sents_b = self._process_sentences(sents_b)
# Build matrix of edit distance ratios
comparisons = self._calculate_ratios(sents_a, sents_b)
return comparisons
def compare_sliding_window(self, str_a, str_b, window_length=50, curse_forward=20):
"""
Compare two strings with a sliding window method based on window_length and curse_forward values
:param string_a: str
:param string_b: str
:param window_length: int
:param curse_forward: int
:return: list [[Comparison]]
"""
if self.stem_words:
stemmer = Stemmer()
str_a = stemmer.stem(str_a)
str_b = stemmer.stem(str_b)
substrs_a = self._str_to_windows(str_a, window_length, curse_forward)
substrs_b = self._str_to_windows(str_b, window_length, curse_forward)
# Build
comparisons = self._calculate_ratios(substrs_a, substrs_b)
return comparisons
def stemmify(self):
"""Returns text with only stems.
An alternate method to lemmatization. Instead of converting to lemmata
(principi -> princeps) converts to stemma (principi -> princp)
Returns:
:obj:`self.__class__` New text with stemma
Example:
>>> text = LatinText('Arma virumque cano, Troiae qui primus ab oris')
>>> print(text.stemmify())
arm vir cano, troi qui prim ab or
""" # noqa
from cltk.stem.latin.stem import Stemmer
return self.__class__(
Stemmer().stem(self.data.lower()),
self.options
)
word_dict[word.lemmatization.lower()][language]\
.occurrences.append(word)
word_dict[word.lemmatization.lower()][language]\
.variations.add(word.text.lower())
word_dict[word.lemmatization.lower()][language]\
.files.add(word.file_name)
word_dict[word.lemmatization.lower()][language]\
.language = word.language
word_dict[word.lemmatization.lower()][language]\
.lemma = word.lemmatization
word_dict[word.lemmatization.lower()][language]\
.regions.add(file_dict[word.file_name].region)
check_suspicious(word_dict[word.lemmatization.lower()][word.language])
#endloop
la_stemmer = Stemmer()
for key in word_dict:
for language in word_dict[key]:
word = word_dict[key][language]
if "transl" in language:
word.frequency_total = \
len(word.occurrences) / translated_occurrence_count
else:
word.frequency_total = \
len(word.occurrences) / untranslated_occurrence_count
word.frequency_language = \
len(word.occurrences) / lang_count[word.language]
if language == "la":
word.stem = la_stemmer.stem(word.lemma)
# Sort according to the given arguments before writing to file
def stem(form):
stemmer = Stemmer()
string = stemmer.stem(form)
return json.dumps(string)
def get(self, sentence):
stemmer = Stemmer()
return {'stemmed_output': stemmer.stem(sentence.lower())}
