def __init__(self, config_path):
print("Initializing configuration for relations generator...")
# Read the config file
self.config = configparser.RawConfigParser()
try:
self.config.read(config_path)
except OSError:
print("Unable to read config, aborting.")
return
# Load and split the POS parameter into a list
# Create a mapping from each possible POS value in the config to the corresponding rwn component
mapping = {"verb": rwn.Synset.Pos.VERB,
"noun": rwn.Synset.Pos.NOUN,
"adverb": rwn.Synset.Pos.ADVERB,
"adjective": rwn.Synset.Pos.ADJECTIVE}
# Keep in the map of PoS : Rwn.Pos only the specified parts of speech
self.pos = mapping
# Load the root of the folders containing constraints
self.constraints_root_path = self.config.get("paths", "CONSTRAINTS_ROOT_PATH")
vocab_path = self.config.get("paths", "VOCAB_PATH")
self.vocabulary = list()
with open(file=vocab_path, mode="r", encoding="utf-8") as vocab_file:
for line in vocab_file:
self.vocabulary.append(line.strip())
self.vocabulary = set(unique(self.vocabulary))
print(f"Loaded {len(self.vocabulary)} words from {vocab_path}")
print(
f"Finished initializing config for relations generator. Will output results to {self.constraints_root_path}")
def augment_antonym_verbs(lemmas: list, times: list) -> list:
"""
Computes the list of valid antonym pairs.
:param lemmas: A list of verb lemmas.
:param times: A list of verbal times.
:return: The list of antonym verb pairs generated from the provided lemmas and verbal times.
"""
wordnet = rwn.RoWordNet()
conjugator = mlconjug.Conjugator(language='ro')
# Enforce lemmas vs use language model to lemmatize. First choice should suffice for now.
# Todo: Compare advantages/disadvantages, maybe implement second
global_antonym_pairs = list()
for lemma in lemmas:
global_antonym_pairs.extend(
generate_conjugated_pairs(lemma, wordnet, conjugator, times,
'ant'))
valid_antonym_pairs = unique(
list([
pair for pair in global_antonym_pairs
if pair[0] is not None and pair[1] is not None
]))
return valid_antonym_pairs
def extract_verb_lemmas(base_langauge_model: str, sentences: list) -> tuple:
verb_lemmas = list()
verb_times = list()
conjugator = mlconjug.Conjugator(language='ro')
# Manually exclude the auxilliary verbs. If the to-do below is solved, this is no longer required
auxilliary_verb_lemmas = ['vrea', 'avea', 'fi']
excluded_verb_forms = ['Vmp--sf', 'Vmp--sm', 'Vmp--pm', 'Vmp--pf']
# Load the SpaCy Language Model
lang_model = spacy.load(base_langauge_model)
for sentence in sentences:
doc = lang_model(sentence)
for token in doc:
# For now this takes all the verbs regardless of their conjugation.
# Todo: Use https://universaldependencies.org/tagset-conversion/ro-multext-uposf.html
if valid_verb(token, auxilliary_verb_lemmas, excluded_verb_forms):
if token.lemma_ not in verb_lemmas:
verb_lemmas.append(token.lemma_)
# Figure the possible conjugations of the lemma which are equal to the token
try:
target_conjugations = conjugator.conjugate(
token.lemma_)
# For each conjugation of the target word
for conjugation in target_conjugations.iterate():
# Unpack the time, mood, person and value from the tuple
time, mood, person, value = conjugation
if value == token.text:
# Found a possible candidate, append the time/mood/person to the list
verb_times.append((time, mood, person))
except ValueError:
print(
f"Unable to conjugate, possibly mistagged verb {token.text}"
)
verb_lemmas = unique(verb_lemmas)
verb_times = unique(verb_times)
print("Verbs extracted", verb_lemmas)
print("Verb times extracted:", verb_times)
return verb_lemmas, verb_times
def generate_antonym_pairs(config: SettingConfig) -> dict:
"""
Generates antonym pairs from RoWordNet.
:param config: Configuration of the current run.
:return: A dictionary where keys are strings representing parts of speech and values are lists of pairs
corresponding to synonyms / antonyms from that category.
"""
print(f"Generating initial antonym pairs from RoWordNet @ {datetime.now()}")
wn = rwn.RoWordNet()
# Create the output dictionary that will be of type dict(str : set(pair(str, str)) where the key is
# the PoS and the value is a set of pairs of words of PoS specified by the key
pairs = dict()
# Iterate over the selected parts of speech
for part_of_speech in config.pos.values():
pos_pairs = list()
# Return all synsets corresponding to the PoS
synset_ids = wn.synsets(pos=part_of_speech)
# Iterate all the synsets for the current PoS
for synset_id in synset_ids:
# Get the synset object specified by synset_id
synset = wn.synset(synset_id)
# Get the outbound relations of type antonym from
outbound_relations = filter(lambda x: x[1] == 'near_antonym', wn.outbound_relations(synset_id))
# Iterate outbound relations
for relation in outbound_relations:
# Get the synset corresponding to the target of the outbound relation
target_synset = wn.synset(relation[0])
# Get all the pairs, sort them by first word to keep set entries unique
current_iteration_pairs = get_cross_synset_pairs(synset, target_synset)
# Add the current set of pairs
pos_pairs.extend(current_iteration_pairs)
# Get corresponding key in pos dictionary and add the pair to the resulting dictionary
for key, value in config.pos.items():
if value == part_of_speech:
pairs[key] = unique(pos_pairs)
# Return the whole dictionary
print(f"Successfully generated antonym paris @ {datetime.now()}")
return pairs
def generate_conjugated_pairs(lemma: str, wordnet: RoWordNet,
conjugator: mlconjug.Conjugator,
valid_verbal_times: list, mode: str) -> list:
"""
Generates the conjugated pairs for a verb.
:param lemma: The lemma (infinitive form) of the target word.
:param wordnet: An instance of the RoWordNet lexicon.
:param conjugator: An instance of the mlConjug conjugator.
:param valid_verbal_times: A list of valid verb times.
:param mode: Specifies whether the function returns the list of antonym pairs or synonym pairs.
:return: A list of conjugated pairs, according to the constraint specified by mode.
"""
# Check the mode
if mode == 'syn':
relationships = generate_rwn_synonyms(wordnet, lemma)
elif mode == 'ant':
relationships = generate_rwn_antonyms(wordnet, lemma)
else:
raise ValueError('Invalid Mode')
pairs = list()
for relationship in relationships:
for pair in relationship:
pairs.append(pair)
pairs = unique(pairs)
pairs = list(filter(None, list(map(lambda x: process_pair(x), pairs))))
conjugated_pairs = list()
for pair in pairs:
for time in valid_verbal_times:
try:
conjugated_first = conjugate(pair[0], conjugator, time)
conjugated_second = conjugate(pair[1], conjugator, time)
if not (conjugated_first is None
and conjugated_second is None):
conjugated_pairs.append(
(conjugated_first, conjugated_second))
except ValueError:
# Well, no big deal if we are unable to conjugate the pairs. Probably the correct place to "solve"
# the error of intruders by simply passing to the next conjugation step and filtering only
# valid elements in the list.
print(f"Value Error when conjugating {pair}")
pass
return conjugated_pairs
def generate_synonym_pairs(config: SettingConfig) -> dict:
"""
Generates synonym pairs from RoWordNet.
:param config: Configuration of the current run.
:return: A dictionary where keys are strings representing parts of speech and values are lists of pairs
corresponding to synonyms / antonyms from that category.
"""
wn = rwn.RoWordNet()
# Create the output dictionary that will be of type dict(str : set(pair(str, str)) where the key is
# the PoS and the value is a set of pairs of words of PoS specified by the key
pairs = dict()
# Iterate over the selected parts of speech
for part_of_speech in config.pos.values():
pos_pairs = list()
# Return all synsets corresponding to the PoS
synset_ids = wn.synsets(pos=part_of_speech)
# Iterate all the synsets for the current PoS
for synset_id in synset_ids:
# Get the synset object specified by synset_id
synset = wn.synset(synset_id)
# Get all the pairs, sort them by first word to keep set entries unique
current_iteration_pairs = get_synset_pairs(synset)
# Append all pairs from the current PoS to the global set
pos_pairs.extend(current_iteration_pairs)
# Get corresponding key in pos dictionary and add the pair to the resulting dictionary
for key, value in config.pos.items():
if value == part_of_speech:
pairs[key] = unique(pos_pairs)
return pairs
def postprocess_pairs(raw_pairs: list, config: SettingConfig) -> list:
"""
Processes a list of pairs to remove reflexive forms, pairs with duplicate elements, proper nouns, etc.
:param raw_pairs: List of tuples representing the initial pairs.
:param config: Configuration of the current run.
:return: The filtered list of pairs from the initial pairs.
"""
processed_pairs = list()
for raw_pair in raw_pairs:
# Preprocess each line
processed_line = process_pair(raw_pair)
# If the processed line is not empty (meaning we have 2 different words separated by a space)
if processed_line:
# Split the words
w1, w2 = processed_line
# Check if both are in the dictionary
if w1 in config.vocabulary and w2 in config.vocabulary:
processed_pairs.append((w1, w2))
return unique(processed_pairs)
def __init__(self, config_path, language_model_name):
# Read the config file
self.config = configparser.RawConfigParser()
try:
self.config.read(config_path)
except OSError:
print("Unable to read config, aborting.")
return
# Read the word vectors path from the config
self.input_vectors_path = self.config.get("paths", "VEC_PATH")
# Read the vocabulary mode (all words or just dataset words) from the config
self.vocab_mode = self.config.get("settings", "VOCABULARY")
if self.vocab_mode == 'all':
self.diacritics = 'True'
vocab_path = self.config.get("paths", "VOCAB_PATH")
elif self.vocab_mode == 'small':
self.diacritics = self.config.get("settings", "DIACRITICS")
if self.diacritics == 'True':
vocab_path = self.config.get("paths",
"VOCAB_PATH_DATASET_DIAC")
else:
vocab_path = self.config.get("paths",
"VOCAB_PATH_DATASET_NODIAC")
else:
print('Wrong value for parameter VOCABULARY in config. Exiting')
return
synonym_paths = list()
antonym_paths = list()
# Read the root path of the constraints files
constraints_root_path = self.config.get("paths",
"CONSTRAINTS_ROOT_PATH")
# Read the PoS variable
self.parts_of_speech = self.config.get("settings", "POS").replace(
"[", "").replace("]", "").replace(" ", "").split(",")
print("Loading constraints...")
# Append antonyms and synonyms of each selected PoS from their respective folder
for part_of_speech in self.parts_of_speech:
antonym_paths.append(
os.path.join(constraints_root_path, part_of_speech,
"antonyms.txt"))
synonym_paths.append(
os.path.join(constraints_root_path, part_of_speech,
"synonyms.txt"))
self.synonyms = to.load_multiple_constraints(synonym_paths)
self.antonyms = to.load_multiple_constraints(antonym_paths)
vsp_path = self.config.get("paths", "VSP_PAIRS_PATH")
self.vsp_pairs = to.load_vsp_pairs(vsp_path)
print("Loaded constraints.")
# Read and parse the mode (whether to include synonyms, antonyms or VSP pairs in the current run)
mode = self.config.get("settings", "MODE").replace("[", "").replace(
"]", "").replace(" ", "").split(",")
vocab = list()
print("Loading vocabulary...")
with open(file=vocab_path, mode="r", encoding="utf-8") as vocab_file:
for line in vocab_file:
vocab.append(line.strip())
# Add augmented words from synonyms list to the vocab
# Small optimization trick for O(1) lookup:
vocab_set = set(vocab)
for pair in self.synonyms:
if pair[0] in vocab_set or pair[1] in vocab_set:
vocab.append(pair[0])
vocab.append(pair[1])
# Add augmented words from antonym lists to the vocab
for pair in self.antonyms:
if pair[0] in vocab_set or pair[1] in vocab_set:
vocab.append(pair[0])
vocab.append(pair[1])
vocab = to.unique(vocab)
print("Loaded vocabulary.")
# Load the word vectors
print("Loading word vectors...")
dimensions, self.vectors = to.load_vectors(self.input_vectors_path,
set(vocab))
# Return if vectors were not successfully loaded
if not self.vectors:
print("Unable to load initial vectors")
return
print("Loaded word vectors ")
if language_model_name:
self.output_vectors_path = f"{self.config.get('paths', 'VEC_ROOT_PATH')}/{language_model_name}.vec"
else:
self.output_vectors_path = self.config.get(
"paths", "CF_VEC_PATH"
).split(
"."
)[0] + f"_{str(datetime.timestamp(datetime.now())).split('.')[0]}.vec"
# The vocabulary contains the keys of vectors successfully loaded by the initial vocabulary: Words in the
# initial vocabulary with no corresponding vector are skipped
self.vocabulary = to.unique(self.vectors.keys())
self.dimensions = f"{len(self.vocabulary)} {dimensions.split(' ')[1]}"
# Load synonym and antonym pairs from the paths specified
self.mode = mode
# Read the hyperparameters of our run
self.hyper_k1 = self.config.getfloat("hyperparameters", "hyper_k1")
self.hyper_k2 = self.config.getfloat("hyperparameters", "hyper_k2")
self.hyper_k3 = self.config.getfloat("hyperparameters", "hyper_k3")
self.sgd_iters = self.config.getint("hyperparameters", "sgd_iters")
self.delta = self.config.getfloat("hyperparameters", "delta")
self.gamma = self.config.getfloat("hyperparameters", "gamma")
self.rho = self.config.getfloat("hyperparameters", "rho")
print(
f"Initialized counterfitting settings. Vocab path: {vocab_path}, PoS paths: {self.parts_of_speech},"
f" Mode: {self.mode}, diacritics: {self.diacritics}."
f" Hyperpameters: {self.hyperparams_tostring()}")
