class RawModelData:
def __init__(self, tagging_order, emission_order):
self.stat = Statistics() # Statistics about trainig
# Címkék ngram modellje
self.tag_ngram_model = NGramModel(tagging_order + 1)
# Eredeti szóalakok és a megelőző cimkék modellje
self.std_emission_ngram_model = NGramModel(emission_order + 1)
# Speciális tokenek és a megelőző cimkék modellje
self.spec_emission_ngram_model = NGramModel(2)
self.eos_tag = None
# Lemma suffix gyakorisági táblázat (HashLemmaTree volt.)
self.lemma_suffix_tree = HashSuffixTree(100)
# Lemma gyakorisági táblázat
self.lemma_freq_tree = HashSuffixTree(5)
# Lemma gyakorisági táblázat
self.lemma_unigram_model = LemmaUnigramModel()
# Szóalakok suffix gyakorisági táblázata kis- és nagybetűérzékenyen.
self.lower_suffix_tree = None # HashSuffixTree(0)
self.upper_suffix_tree = None # HashSuffixTree(0)
self.lemma_lambdas = list()
# LogLinearBiCombiner a guesserből és az unigram modellből származó adatok kombinálásához.
from purepos.model.combiner import default_combiner
self.combiner = default_combiner()
def compile(self) -> CompiledModelData:
c = CompiledModelData()
c.unigram_lemma_model = self.lemma_unigram_model
c.tag_transition_model = self.tag_ngram_model.create_probability_model(
)
c.standard_emission_model = self.std_emission_ngram_model.create_probability_model(
)
c.spec_tokens_emission_model = self.spec_emission_ngram_model.create_probability_model(
)
c.apriori_tag_probs = self.tag_ngram_model.word_apriori_probs()
theta = HashSuffixTree.calculate_theta(c.apriori_tag_probs)
c.lower_case_suffix_guesser = self.lower_suffix_tree.create_guesser(
theta)
c.upper_case_suffix_guesser = self.upper_suffix_tree.create_guesser(
theta)
c.lemma_guesser = self.lemma_suffix_tree.create_guesser(theta)
c.suffix_lemma_model = self.lemma_freq_tree.create_guesser(theta)
c.combiner = self.combiner
return c
def build_suffix_trees(self):
# Tanuláskor, beolvasás után suffixtree-k építése.
self.raw_model_data.lower_suffix_tree = HashSuffixTree(self.data.suffix_length)
self.raw_model_data.upper_suffix_tree = HashSuffixTree(self.data.suffix_length)
for word, m in self.data.standard_tokens_lexicon.representation.items():
word_freq = self.data.standard_tokens_lexicon.word_count(word)
if word_freq <= self.data.rare_frequency:
lower_word = word.lower()
islower = lower_word == word
for tag in m.keys():
word_tag_freq = self.data.standard_tokens_lexicon.wordcount_for_tag(word, tag)
if islower:
self.raw_model_data.lower_suffix_tree.add_word(
lower_word, tag, word_tag_freq)
self.raw_model_data.stat.increment_lower_guesser_items(word_tag_freq)
else:
self.raw_model_data.upper_suffix_tree.add_word(
lower_word, tag, word_tag_freq)
self.raw_model_data.stat.increment_upper_guesser_items(word_tag_freq)
def __init__(self, tagging_order, emission_order):
self.stat = Statistics() # Statistics about trainig
# Címkék ngram modellje
self.tag_ngram_model = NGramModel(tagging_order + 1)
# Eredeti szóalakok és a megelőző cimkék modellje
self.std_emission_ngram_model = NGramModel(emission_order + 1)
# Speciális tokenek és a megelőző cimkék modellje
self.spec_emission_ngram_model = NGramModel(2)
self.eos_tag = None
# Lemma suffix gyakorisági táblázat (HashLemmaTree volt.)
self.lemma_suffix_tree = HashSuffixTree(100)
# Lemma gyakorisági táblázat
self.lemma_freq_tree = HashSuffixTree(5)
# Lemma gyakorisági táblázat
self.lemma_unigram_model = LemmaUnigramModel()
# Szóalakok suffix gyakorisági táblázata kis- és nagybetűérzékenyen.
self.lower_suffix_tree = None # HashSuffixTree(0)
self.upper_suffix_tree = None # HashSuffixTree(0)
self.lemma_lambdas = list()
# LogLinearBiCombiner a guesserből és az unigram modellből származó adatok kombinálásához.
from purepos.model.combiner import default_combiner
self.combiner = default_combiner()
def __init__(self, tagging_order, emission_order):
self.stat = Statistics() # Statistics about trainig
# Címkék ngram modellje
self.tag_ngram_model = NGramModel(tagging_order + 1)
# Eredeti szóalakok és a megelőző cimkék modellje
self.std_emission_ngram_model = NGramModel(emission_order + 1)
# Speciális tokenek és a megelőző cimkék modellje
self.spec_emission_ngram_model = NGramModel(2)
self.eos_tag = None
# Lemma suffix gyakorisági táblázat (HashLemmaTree volt.)
self.lemma_suffix_tree = HashSuffixTree(100)
# Lemma gyakorisági táblázat
self.lemma_freq_tree = HashSuffixTree(5)
# Lemma gyakorisági táblázat
self.lemma_unigram_model = LemmaUnigramModel()
# Szóalakok suffix gyakorisági táblázata kis- és nagybetűérzékenyen.
self.lower_suffix_tree = None # HashSuffixTree(0)
self.upper_suffix_tree = None # HashSuffixTree(0)
self.lemma_lambdas = list()
# LogLinearBiCombiner a guesserből és az unigram modellből származó adatok kombinálásához.
from purepos.model.combiner import default_combiner
self.combiner = default_combiner()
class RawModelData:
def __init__(self, tagging_order, emission_order):
self.stat = Statistics() # Statistics about trainig
# Címkék ngram modellje
self.tag_ngram_model = NGramModel(tagging_order + 1)
# Eredeti szóalakok és a megelőző cimkék modellje
self.std_emission_ngram_model = NGramModel(emission_order + 1)
# Speciális tokenek és a megelőző cimkék modellje
self.spec_emission_ngram_model = NGramModel(2)
self.eos_tag = None
# Lemma suffix gyakorisági táblázat (HashLemmaTree volt.)
self.lemma_suffix_tree = HashSuffixTree(100)
# Lemma gyakorisági táblázat
self.lemma_freq_tree = HashSuffixTree(5)
# Lemma gyakorisági táblázat
self.lemma_unigram_model = LemmaUnigramModel()
# Szóalakok suffix gyakorisági táblázata kis- és nagybetűérzékenyen.
self.lower_suffix_tree = None # HashSuffixTree(0)
self.upper_suffix_tree = None # HashSuffixTree(0)
self.lemma_lambdas = list()
# LogLinearBiCombiner a guesserből és az unigram modellből származó adatok kombinálásához.
from purepos.model.combiner import default_combiner
self.combiner = default_combiner()
def compile(self) -> CompiledModelData:
c = CompiledModelData()
c.unigram_lemma_model = self.lemma_unigram_model
c.tag_transition_model = self.tag_ngram_model.create_probability_model()
c.standard_emission_model = self.std_emission_ngram_model.create_probability_model()
c.spec_tokens_emission_model = self.spec_emission_ngram_model.create_probability_model()
c.apriori_tag_probs = self.tag_ngram_model.word_apriori_probs()
theta = HashSuffixTree.calculate_theta(c.apriori_tag_probs)
c.lower_case_suffix_guesser = self.lower_suffix_tree.create_guesser(theta)
c.upper_case_suffix_guesser = self.upper_suffix_tree.create_guesser(theta)
c.lemma_guesser = self.lemma_suffix_tree.create_guesser(theta)
c.suffix_lemma_model = self.lemma_freq_tree.create_guesser(theta)
c.combiner = self.combiner
return c
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)
def compile(self) -> CompiledModelData:
c = CompiledModelData()
c.unigram_lemma_model = self.lemma_unigram_model
c.tag_transition_model = self.tag_ngram_model.create_probability_model()
c.standard_emission_model = self.std_emission_ngram_model.create_probability_model()
c.spec_tokens_emission_model = self.spec_emission_ngram_model.create_probability_model()
c.apriori_tag_probs = self.tag_ngram_model.word_apriori_probs()
theta = HashSuffixTree.calculate_theta(c.apriori_tag_probs)
c.lower_case_suffix_guesser = self.lower_suffix_tree.create_guesser(theta)
c.upper_case_suffix_guesser = self.upper_suffix_tree.create_guesser(theta)
c.lemma_guesser = self.lemma_suffix_tree.create_guesser(theta)
c.suffix_lemma_model = self.lemma_freq_tree.create_guesser(theta)
c.combiner = self.combiner
return c
def compile(self) -> CompiledModelData:
c = CompiledModelData()
c.unigram_lemma_model = self.lemma_unigram_model
c.tag_transition_model = self.tag_ngram_model.create_probability_model(
)
c.standard_emission_model = self.std_emission_ngram_model.create_probability_model(
)
c.spec_tokens_emission_model = self.spec_emission_ngram_model.create_probability_model(
)
c.apriori_tag_probs = self.tag_ngram_model.word_apriori_probs()
theta = HashSuffixTree.calculate_theta(c.apriori_tag_probs)
c.lower_case_suffix_guesser = self.lower_suffix_tree.create_guesser(
theta)
c.upper_case_suffix_guesser = self.upper_suffix_tree.create_guesser(
theta)
c.lemma_guesser = self.lemma_suffix_tree.create_guesser(theta)
c.suffix_lemma_model = self.lemma_freq_tree.create_guesser(theta)
c.combiner = self.combiner
return c
