Dive into secure and efficient coding practices with our curated list of the top 10 examples showcasing 'snowballstemmer' in functional components in Python. Our advanced machine learning engine meticulously scans each line of code, cross-referencing millions of open source libraries to ensure your implementation is not just functional, but also robust and secure. Elevate your React applications to new heights by mastering the art of handling side effects, API calls, and asynchronous operations with confidence and precision.
def main():
argv = sys.argv
if len(argv) < 2:
usage()
return
algorithm = 'english'
if len(argv) > 2:
algorithm = argv[1]
argv = argv[2:]
else:
argv = argv[1:]
stemmer = snowballstemmer.stemmer(algorithm)
splitter = re.compile(r"[\s\.-]")
for arg in argv:
for word in splitter.split(arg):
if word == '':
continue
original = word.lower()
print(original + " -> " + stemmer.stemWord(original))
main()
def init(self, options: Dict) -> None:
self.stemmer = snowballstemmer.stemmer('turkish')
def init(self, options: Dict) -> None:
self.stemmer = snowballstemmer.stemmer('swedish')
def init(self, options: Dict) -> None:
self.stemmer = snowballstemmer.stemmer('romanian')
def init(self, options: Dict) -> None:
self.stemmer = snowballstemmer.stemmer('hungarian')
'twas,us,wants,was,we,were,what,when,where,which,while,who,whom,why,'
'will,with,would,yet,you,your').lower().split(',')
def is_stopword(str):
'''文字がストップワードかどうかを返す
大小文字は同一視する
戻り値:
ストップワードならTrue、違う場合はFalse
'''
return str.lower() in stop_words
# 素性抽出
stemmer = snowballstemmer.stemmer('english')
word_counter = Counter()
with codecs.open(fname_sentiment, 'r', fencoding) as file_in:
for line in file_in:
for word in line[3:].split(' '): # line[3:]で極性ラベル除去
# 前後の空白文字除去
word = word.strip()
# ストップワード除去
if is_stopword(word):
continue
# ステミング
word = stemmer.stemWord(word)
'''
serialVersionUID = 1
a_0 = [
Among(u"", -1, 6),
Among(u"U", 0, 2),
Among(u"Y", 0, 1),
Among(u"\u00E4", 0, 3),
Among(u"\u00F6", 0, 4),
Among(u"\u00FC", 0, 5)
]
a_1 = [
Among(u"e", -1, 2),
Among(u"em", -1, 1),
Among(u"en", -1, 2),
Among(u"ern", -1, 1),
Among(u"er", -1, 1),
Among(u"s", -1, 3),
Among(u"es", 5, 2)
]
a_2 = [
Among(u"en", -1, 1),
Among(u"er", -1, 1),
Among(u"st", -1, 2),
Among(u"est", 2, 1)
]
a_3 = [
Among(u"ig", -1, 1),
Among(u"lich", -1, 1)
Among(u"imento", -1, 6),
Among(u"ivo", -1, 9),
Among(u"it\u00E0", -1, 8),
Among(u"ist\u00E0", -1, 1),
Among(u"ist\u00E8", -1, 1),
Among(u"ist\u00EC", -1, 1)
]
a_7 = [
Among(u"isca", -1, 1),
Among(u"enda", -1, 1),
Among(u"ata", -1, 1),
Among(u"ita", -1, 1),
Among(u"uta", -1, 1),
Among(u"ava", -1, 1),
Among(u"eva", -1, 1),
Among(u"iva", -1, 1),
Among(u"erebbe", -1, 1),
Among(u"irebbe", -1, 1),
Among(u"isce", -1, 1),
Among(u"ende", -1, 1),
Among(u"are", -1, 1),
Among(u"ere", -1, 1),
Among(u"ire", -1, 1),
Among(u"asse", -1, 1),
Among(u"ate", -1, 1),
Among(u"avate", 16, 1),
Among(u"evate", 16, 1),
Among(u"ivate", 16, 1),
Among(u"ete", -1, 1),
Among(u"erete", 20, 1),
Among(u"irete", 20, 1),
serialVersionUID = 1
a_0 = [
Among(u"", -1, 6),
Among(u"\u00E1", 0, 1),
Among(u"\u00E9", 0, 2),
Among(u"\u00ED", 0, 3),
Among(u"\u00F3", 0, 4),
Among(u"\u00FA", 0, 5)
]
a_1 = [
Among(u"la", -1, -1),
Among(u"sela", 0, -1),
Among(u"le", -1, -1),
Among(u"me", -1, -1),
Among(u"se", -1, -1),
Among(u"lo", -1, -1),
Among(u"selo", 5, -1),
Among(u"las", -1, -1),
Among(u"selas", 7, -1),
Among(u"les", -1, -1),
Among(u"los", -1, -1),
Among(u"selos", 10, -1),
Among(u"nos", -1, -1)
]
a_2 = [
Among(u"ando", -1, 6),
Among(u"iendo", -1, 6),
Among(u"yendo", -1, 7),
Among(u"\u00E1ndo", -1, 2),
a_1 = [
Among(u"", -1, 3),
Among(u"a~", 0, 1),
Among(u"o~", 0, 2)
]
a_2 = [
Among(u"ic", -1, -1),
Among(u"ad", -1, -1),
Among(u"os", -1, -1),
Among(u"iv", -1, 1)
]
a_3 = [
Among(u"ante", -1, 1),
Among(u"avel", -1, 1),
Among(u"\u00EDvel", -1, 1)
]
a_4 = [
Among(u"ic", -1, 1),
Among(u"abil", -1, 1),
Among(u"iv", -1, 1)
]
a_5 = [
Among(u"ica", -1, 1),
Among(u"\u00E2ncia", -1, 1),
Among(u"\u00EAncia", -1, 4),
Among(u"ira", -1, 9),
Among(u"adora", -1, 1),
Among(u"osa", -1, 1),