def read(self, text: str):
sentences = list()
for sent in text.split(self.linesep + self.linesep):
if len(sent)-1 > 0:
sentences.append(self.sentence_parser.read(sent))
paragraph = Paragraph(sentences)
document = Document()
document.append(paragraph)
return document
def read(self, text: str):
# it parses the whole(!) analysed corpus
sentences = list()
for line in text.split(self.linesep):
if len(line) > 0:
sentences.append(self.sentence_parser.read(line))
paragraph = Paragraph(sentences)
document = Document()
document.append(paragraph)
return document
def train(self, document: Document):
# todo read lines by lines. See the issue:
# https://github.com/ppke-nlpg/purepos-python3/issues/5
self.raw_model_data.eos_tag = self.data.tag_vocabulary.add_element(ModelData.EOS_TAG)
for sentence in document.sentences():
mysentence = Sentence(sentence)
self.add_sentence_markers(mysentence)
self.add_sentence(mysentence)
self.build_suffix_trees()
self.raw_model_data.combiner.calculate_params(document, self.raw_model_data, self.data)
def calculate_params(self, doc: Document,
raw_modeldata: RawModelData,
modeldata: ModelData):
apriori_probs = raw_modeldata.tag_ngram_model.word_apriori_probs()
theta = HashSuffixTree.calculate_theta(apriori_probs)
lemma_suffix_guesser = raw_modeldata.lemma_suffix_tree.create_guesser(theta)
lemma_prob = raw_modeldata.lemma_freq_tree.create_guesser(theta)
lemma_unigram_model = raw_modeldata.lemma_unigram_model
lambda_s = 1.0
lambda_u = 1.0
lambda_l = 1.0
for sentence in doc.sentences():
for tok in sentence:
suffix_probs = lemma.batch_convert(lemma_suffix_guesser.tag_log_probabilities(
tok.token), tok.token, modeldata.tag_vocabulary)
uni_probs = dict()
for t in suffix_probs.keys():
uniscore = lemma_unigram_model.log_prob(t.stem)
uni_probs[t] = uniscore
lemma_probs = dict()
for t in suffix_probs.keys():
lemma_score = lemma_prob.tag_log_probability(t.stem, lemma.main_pos_tag(t.tag))
lemma_probs[t] = lemma_score
uni_max = max(uni_probs.items(), key=lambda e: e[1])
t = max(suffix_probs.items(), key=lambda e: e[1][1])
suffix_max = (t[0], t[1][1])
lemma_max = max(lemma_probs.items(), key=lambda e: e[1])
act_uni_prob = lemma_unigram_model.log_prob(tok.stem)
act_lemma_prob = lemma_prob.tag_log_probability(tok.stem, lemma.main_pos_tag(
tok.tag))
if tok in suffix_probs.keys():
act_suff_prob = suffix_probs[tok][1]
else:
act_suff_prob = UNKOWN_VALUE
uni_prop = act_uni_prob - uni_max[1]
suff_prop = act_suff_prob - suffix_max[1]
lemma_prop = act_lemma_prob - lemma_max[1]
if uni_prop > suff_prop and uni_prop > lemma_prop:
lambda_u += uni_prop
elif suff_prop > uni_prop and suff_prop > lemma_prop:
lambda_s += suff_prop
elif lemma_prop > uni_prop and lemma_prop > suff_prop:
lambda_l += lemma_prop
s = lambda_u + lambda_s + lambda_l
lambda_u /= s
lambda_s /= s
lambda_l /= s
self.lambdas.append(lambda_u)
self.lambdas.append(lambda_s)
self.lambdas.append(lambda_l)