A vocabulary that can automatically evaluate a list of Stop-Words.
An entry 'w' is relevant for the speaker if its probability is less than a threshold:
| P(w) <= 1 / (alpha * V)
where 'alpha' is an empirical coefficient and 'V' is the vocabulary size of the speaker.
Create a new StopWords instance.
def __init__(self, case_sensitive=False):
"""Create a new StopWords instance.
:param case_sensitive: (bool) Considers the case of entries or not.
"""
super(StopWords, self).__init__(filename=None, nodump=True, case_sensitive=case_sensitive)
self.__alpha = 0.5
self.__threshold = 0.0
self.__v = 0.0
Return the value of alpha coefficient (float).
def get_alpha(self):
"""Return the value of alpha coefficient (float)."""
return self.__alpha
Return the last estimated threshold (float).
def get_threshold(self):
"""Return the last estimated threshold (float)."""
return self.__threshold
Return the last estimated vocabulary size (int).
def get_v(self):
"""Return the last estimated vocabulary size (int)."""
return self.__v
Fix the alpha option.
Alpha is a coefficient to add specific stop-words in the list. Default value is 0.5.
def set_alpha(self, alpha):
"""Fix the alpha option.
Alpha is a coefficient to add specific stop-words in the list.
Default value is 0.5.
:param alpha: (float) Value in range [0..4]
"""
alpha = float(alpha)
if 0.0 < alpha <= self.MAX_ALPHA:
self.__alpha = alpha
else:
raise IndexRangeException(alpha, 0, StopWords.MAX_ALPHA)
Make a deep copy of the instance.
def copy(self):
"""Make a deep copy of the instance.
:returns: (StopWords)
"""
s = StopWords()
for i in self:
s.add(i)
s.set_alpha(self.__alpha)
return s
Load a list of stop-words from a file.
def load(self, filename, merge=True):
"""Load a list of stop-words from a file.
:param filename: (str)
:param merge: (bool) Merge with the existing list (if True) or
delete the existing list (if False)
"""
if merge is False:
self.clear()
self.load_from_ascii(filename)
Add entries to the list of stop-words from the content of a tier.
Estimate if a token is relevant: if not it adds it in the stop-list.
EmptyInputError, TooSmallInputError
def evaluate(self, tier=None, merge=True):
"""Add entries to the list of stop-words from the content of a tier.
Estimate if a token is relevant: if not it adds it in the stop-list.
:param tier: (sppasTier) A tier with entries to be analyzed.
:param merge: (bool) Merge with the existing list (if True) or
delete the existing list and create a new one (if False)
:returns: (int) Number of entries added into the list
:raises: EmptyInputError, TooSmallInputError
"""
if tier is None or tier.is_empty():
raise EmptyInputError(tier.get_name())
if len(tier) < StopWords.MIN_ANN_NUMBER:
raise TooSmallInputError(tier.get_name())
unigram = sppasUnigram()
for ann in tier:
for label in ann.get_labels():
tag = label.get_best()
content = tag.get_content()
if content not in symbols.all:
unigram.add(content)
self.__v = len(unigram)
self.__threshold = 1.0 / (self.__alpha * float(self.__v))
if merge is False:
self.clear()
usum = float(unigram.get_sum())
nb = 0
for token in unigram.get_tokens():
p_w = float(unigram.get_count(token)) / usum
if p_w > self.__threshold:
self.add(token)
nb += 1
return nb