def train_mimic_model(polyglot_embedding_path: str, mimic_model_path: str,
max_word_length: int, num_epochs: int,
learning_rate: float, use_dev_set: bool):
full_embedding = PolyEmbedding.load(str(polyglot_embedding_path))
embedding_size = len(full_embedding.zero_vector())
all_X, all_Y = compose_dataset(full_embedding, max_word_length)
if use_dev_set:
train_X = all_X[TEST_SET_SIZE:]
train_Y = all_Y[TEST_SET_SIZE:]
validation_data = (all_X[:TEST_SET_SIZE], all_Y[:TEST_SET_SIZE])
else:
train_X, train_Y = all_X, all_Y
validation_data = None
model = create_mimic_model(max_word_length, embedding_size)
optimizer = optimizers.Adam(lr=learning_rate)
model.compile(optimizer, loss=mse_loss)
if os.path.exists(mimic_model_path):
model.load_weights(mimic_model_path)
loss_to_monitor = 'val_loss' if use_dev_set else 'loss'
save_model = ModelCheckpoint(mimic_model_path,
verbose=1,
monitor=loss_to_monitor,
save_best_only=True)
lr_reducer = ReduceLROnPlateau(verbose=1,
factor=0.2,
min_lr=1e-7,
monitor=loss_to_monitor,
cooldown=100)
model.fit(train_X,
train_Y,
batch_size=1024,
epochs=num_epochs,
callbacks=[save_model, lr_reducer],
validation_data=validation_data)
def cache(self, name, cache, url=None, max_vectors=None):
if self.emb_format in ['polyglot', 'glove']:
from polyglot.mapping import Embedding
if self.emb_format == 'polyglot':
embeddings = Embedding.load(name)
else:
embeddings = Embedding.from_glove(name)
self.itos = embeddings.vocabulary.id_word
self.stoi = embeddings.vocabulary.word_id
self.dim = embeddings.shape[1]
self.vectors = torch.Tensor(embeddings.vectors).view(-1, self.dim)
elif self.emb_format in ['word2vec', 'fasttext']:
try:
from gensim.models import KeyedVectors
except ImportError:
logging.error('Please install `gensim` package first.')
embeddings = KeyedVectors.load_word2vec_format(
name, unicode_errors='ignore', binary=self.binary)
self.itos = embeddings.index2word
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.dim = embeddings.vector_size
self.vectors = torch.Tensor(embeddings.vectors).view(-1, self.dim)
elif self.emb_format == 'fonseca':
import numpy as np
import os
embeddings = np.load(os.path.join(name, 'types-features.npy'))
texts = open(os.path.join(name, 'vocabulary.txt'), 'r').read()
words = set([w.strip() for w in texts.split('\n')])
self.itos = list(words)
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.dim = embeddings.shape[1]
self.vectors = torch.Tensor(embeddings).view(-1, self.dim)
self.unk_vector = self.vectors.mean(0).unsqueeze(0)
def __init__(self, student_summary=[]):
self.punctuations = ['.', ',', '[', ']', '(', ')']
self.stop_words = [
"i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you",
"your", "yours", "yourself", "yourselves", "he", "him", "his",
"himself", "she", "her", "hers", "herself", "it", "its", "itself",
"they", "them", "their", "theirs", "themselves", "what", "which",
"who", "whom", "this", "that", "these", "those", "am", "is", "are",
"was", "were", "be", "been", "being", "have", "has", "had",
"having", "do", "does", "did", "doing", "a", "an", "the", "and",
"but", "if", "or", "because", "as", "until", "while", "of", "at",
"by", "for", "with", "about", "against", "between", "into",
"through", "during", "before", "after", "above", "below", "to",
"from", "up", "down", "in", "out", "on", "off", "over", "under",
"again", "further", "then", "once", "here", "there", "when",
"where", "why", "how", "all", "any", "both", "each", "few", "more",
"most", "other", "some", "such", "no", "nor", "not", "only", "own",
"same", "so", "than", "too", "very", "s", "t", "can", "will",
"just", "don", "should", "now"
]
self.student_summary = [self.clean_doc(s) for s in student_summary]
self.embeddings = Embedding.load("data/embeddings_pkl.tar.bz2")
self.summary_vetors = []
for summary in self.student_summary:
self.summary_vetors.append(self.calculate_doc2vec(summary))
def getEmbeddings(lng):
if lng not in EMBEDDINGS:
home = expanduser("~")
embeddings = Embedding.load(home + "/polyglot_data/embeddings2/" +
lng + "/embeddings_pkl.tar.bz2")
embeddings.apply_expansion(CaseExpander)
EMBEDDINGS[lng] = embeddings
return EMBEDDINGS[lng]
def cache(self, name, cache, url=None, max_vectors=None):
if self.emb_format in ['polyglot', 'glove']:
try:
from polyglot.mapping import Embedding
except ImportError:
logger.error('Please install `polyglot` package first.')
return None
if self.emb_format == 'polyglot':
embeddings = Embedding.load(name)
else:
embeddings = Embedding.from_glove(name)
self.itos = embeddings.vocabulary.id_word
self.stoi = embeddings.vocabulary.word_id
self.dim = embeddings.shape[1]
self.vectors = torch.Tensor(embeddings.vectors).view(-1, self.dim)
elif self.emb_format in ['word2vec', 'fasttext']:
try:
from gensim.models import KeyedVectors
except ImportError:
logger.error('Please install `gensim` package first.')
return None
embeddings = KeyedVectors.load_word2vec_format(
name, unicode_errors='ignore', binary=self.binary
)
self.itos = embeddings.index2word
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.dim = embeddings.vector_size
self.vectors = torch.Tensor(embeddings.vectors).view(-1, self.dim)
elif self.emb_format == 'text':
tokens = []
vectors = []
if self.binary:
import pickle
# vectors should be a dict mapping str keys to numpy arrays
with open(name, 'rb') as f:
d = pickle.load(f)
tokens = list(d.keys())
vectors = list(d.values())
else:
# each line should contain a token and its following fields
# <token> <vector_value_1> ... <vector_value_n>
with open(name, 'r', encoding='utf8') as f:
for line in f:
if line: # ignore empty lines
fields = line.rstrip().split()
tokens.append(fields[0])
vectors.append(list(map(float, fields[1:])))
self.itos = tokens
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.vectors = torch.Tensor(vectors)
self.dim = self.vectors.shape[1]
def external_polygot_embedding(ver):
ver = ver.replace('hn', 'hi')
home = path.expanduser('~')
emb = Embedding.load(
path.join(home,
'polyglot_data/embeddings2/%s/embeddings_pkl.tar.bz2' % ver))
word_idx = {w: i for i, w in enumerate(emb.words)}
if (ver == 'hi'):
word_idx = transdict_stl(word_idx)
embedding = emb.vectors
return embedding, word_idx
def loadembedding(filename):
"""Loads a precomputed embedding into memory
Input:
filename: of the model file
Output:
embedding object
"""
embedding = Embedding.load(filename)
# Apply useful extensions
embedding.apply_expansion(DigitExpander)
# We might need this if we want to ignore case
# embedding.apply_expansion(CaseExpander)
return embedding
def train_word_embeddings(train, polyglot_data):
embeddings = Embedding.load(polyglot_data)
zpadd = [0] * 64
train_embds = []
for t in train:
t_e = []
for w in t:
if w == u'*':
t_e.append(zpadd)
else:
e = embeddings.get(w)
if e is not None:
t_e.append(e)
else:
t_e.append(zpadd)
train_embds.append(t_e)
return train_embds
def train_word_embeddings(train):
embeddings = Embedding.load(
"/home/amir/polyglot_data/embeddings2/fa/embeddings_pkl.tar.bz2")
zpadd = [0] * 64
train_embds = []
for t in train:
t_e = []
for w in t:
if w == u'*':
t_e.append(zpadd)
else:
e = embeddings.get(w)
if e is not None:
t_e.append(e)
else:
t_e.append(zpadd)
train_embds.append(t_e)
return train_embds
def create_list_file():
try:
embeddings = Embedding.load(
os.path.join(DOWNLOAD_DIR,
"embeddings2/en/embeddings_pkl.tar.bz2"))
except Exception as e:
print e.message
ActivityLog.objects.create_log(
None,
level='C',
view_name='scrappers_miners.utils.utils.create_list_file',
message='Error in loading library (polyglot) - %s' % e.message,
traceback=traceback.format_exc())
return False
else:
neighbors = []
for word in FILTER_LIST_WORDS:
try:
neighbors += embeddings.nearest_neighbors(
word, top_k=NEAREST_NEIGHBORS)
except Exception as e:
ActivityLog.objects.create_log(
None,
level='W',
view_name='scrappers_miners.utils.utils.create_list_file',
message=
'Error in finding neighbors of a word in FILTER_LIST_WORDS with a message - %s'
% e.message,
traceback=traceback.format_exc())
filter_words_file = open(FILTER_WORD_FILE_PATH, 'w')
for n in set(neighbors + FILTER_LIST_WORDS):
filter_words_file.write(n.lower() + '\n')
filter_words_file.close()
return True
def _extract_we_polyglot(output_file, vocab_file, we_dic):
#vocabulary
vocabf = codecs.open(vocab_file, "r", "utf-8")
vocab = []
vecList = []
for line in vocabf:
vocab.append(line.split(" ")[0])
#export
embeddings = Embedding.load(we_dic)
f = codecs.open(output_file, "w", "utf-8")
for token in vocab:
token = token.decode("utf-8")
if token in embeddings:
vector = embeddings[token].tolist()
vector.insert(0, token)
vecList.append(vector)
else:
print "====", token
f.write("\n".join(" ".join(map(str, x)) for x in vecList))
f.close()
vocabf.close()
def loadExternalTools(self):
### Load external tools ###
# get ContoPt
wordnetLoadTimeStart = time.time()
wordnet = ContoPtReader.ContoPtLoader()
elapsedTimeWordnetLoad = time.time() - wordnetLoadTimeStart
print "\nWordnet loaded in " + str(elapsedTimeWordnetLoad) + " sec.]\n"
# get word2vec model
wordEmbeddingLoadTimeStart = time.time()
wordEmbeddingsModel = Embedding.load(
parameters.paths["wordEmbeddings"] + "/polyglot-pt.pkl")
#wordEmbeddingsModel = (self.wordEmbeddingsModel).normalize_words()
elapsedTimeWordEmbeddingLoad = time.time() - wordEmbeddingLoadTimeStart
print "\nWord2vec model loaded in " + str(
elapsedTimeWordEmbeddingLoad) + " sec.]\n"
return (wordnet, wordEmbeddingsModel)
def load_embeddings(data_root, languages):
return {
l: Embedding.load(data_root + (f"embeddings/{l}.tar.bz2"))
for l in languages
}
def load(cls, polyglot_embedding_path: str, mimic_model_path: str):
e = PolyEmbedding.load(polyglot_embedding_path)
model = load_model(mimic_model_path, compile=False)
return cls(e, model)
def cache(self, name, cache, url=None, max_vectors=None):
if self.emb_format == 'polyglot':
try:
from polyglot.mapping import Embedding
except ImportError:
logger.error('Please install `polyglot` package first.')
return None
embeddings = Embedding.load(name)
self.itos = embeddings.vocabulary.id_word
self.stoi = embeddings.vocabulary.word_id
self.dim = embeddings.shape[1]
self.vectors = torch.tensor(embeddings.vectors).view(-1, self.dim)
elif self.emb_format == 'glove':
itos = []
vectors = []
with open(name, 'r', encoding='utf8') as f:
for line in f:
try:
values = line.rstrip().split()
itos.append(values[0])
vectors.append([float(x) for x in values[1:]])
except ValueError as e:
# ignore entries that look like:
# by [email protected] 0.6882 -0.36436 ...
continue
self.itos = itos
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.dim = len(vectors[0])
self.vectors = torch.tensor(vectors).view(-1, self.dim)
elif self.emb_format == 'fasttext':
try:
from gensim.models import FastText
except ImportError:
logger.error('Please install `gensim` package first.')
return None
self.vectors = FastText.load_fasttext_format(name)
self.itos = list(self.vectors.wv.vocab.keys())
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.unk_vector = self.vectors['<unk>']
self.dim = self.vectors.vector_size
elif self.emb_format == 'word2vec':
try:
from gensim.models import KeyedVectors
except ImportError:
logger.error('Please install `gensim` package first.')
return None
embeddings = KeyedVectors.load_word2vec_format(
name, unicode_errors='ignore', binary=self.binary)
self.itos = embeddings.index2word
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.dim = embeddings.vector_size
self.vectors = torch.tensor(embeddings.vectors).view(-1, self.dim)
elif self.emb_format == 'text':
tokens = []
vectors = []
if self.binary:
import pickle
# vectors should be a dict mapping str keys to numpy arrays
with open(name, 'rb') as f:
d = pickle.load(f)
tokens = list(d.keys())
vectors = list(d.values())
else:
# each line should contain a token and its following fields
# <token> <vector_value_1> ... <vector_value_n>
with open(name, 'r', encoding='utf8') as f:
for line in f:
if line: # ignore empty lines
fields = line.rstrip().split()
tokens.append(fields[0])
vectors.append(list(map(float, fields[1:])))
self.itos = tokens
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.vectors = torch.tensor(vectors)
self.dim = self.vectors.shape[1]
elif self.emb_format == 'fonseca':
import numpy as np
import os
embeddings = np.load(os.path.join(name, 'types-features.npy'))
texts = open(os.path.join(name, 'vocabulary.txt'), 'r').read()
words = set([w.strip() for w in texts.split('\n')])
self.itos = list(words)
self.stoi = dict(zip(self.itos, range(len(self.itos))))
self.dim = embeddings.shape[1]
self.vectors = torch.tensor(embeddings).view(-1, self.dim)
if self.unk_vector is None:
self.unk_vector = self.vectors.mean(0).unsqueeze(0)
def test_polyglot(self) :
from polyglot.mapping import Embedding
embeddings = Embedding.load("/home/rmyeid/polyglot_data/embeddings2/en/embeddings_pkl.tar.bz2")
neighbors = embeddings.nearest_neighbors("green")
def categorize_tweets(currentTwitterAccount, n_max_tweets=5, settings=None):
if not settings:
settings = load_from_config()
subscription_key = settings["subscription_key"]
api_url = "https://westcentralus.api.cognitive.microsoft.com/text/analytics/v2.0/"
key_phrase_api_url = api_url + "keyPhrases"
language_api_url = api_url + "languages"
embeddings = Embedding.load(settings["model_location"])
consumer_key = settings["consumer_key"]
consumer_secret = settings["consumer_secret"]
access_token = settings["access_token"]
access_token_secret = settings["access_token_secret"]
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
# Fetch swedish tweets
def language_check(string):
headers = {"Ocp-Apim-Subscription-Key": subscription_key}
response = requests.post(language_api_url, headers=headers, json={"documents": [{"id": 1, "text": string}]})
if response.ok:
return response.json()["documents"][0]["detectedLanguages"][0]["iso6391Name"]
else:
if response.status_code == 429:
time.sleep(1)
return language_check(string)
response.raise_for_status()
documents = {"documents": []}
tweets_raw = []
i = 0
for tweet in tweepy.Cursor(api.user_timeline, id=currentTwitterAccount, tweet_mode="extended").items(n_max_tweets):
# removing the http link at the end of the text
result = re.sub(r"http\S+", "", tweet.full_text)
if language_check(result) == "sv":
documents['documents'].append({'id': i, 'language': 'sv', 'text': result})
tweets_raw.append((result, tweet.created_at))
i += 1
### Extract key words
headers = {"Ocp-Apim-Subscription-Key": subscription_key}
response = requests.post(key_phrase_api_url, headers=headers, json=documents)
key_phrases = response.json()
# Parse key words
key_words = [[y for y in x.values()][0] for x in key_phrases["documents"]]
key_words = [[y.split(" ") for y in x] for x in key_words]
key_words = [[y.strip() for sublist in l for y in sublist] for l in key_words]
### Determine closest category for the sets of key words
def embedding_distances(word, category): # Adapter to handle missing words for embedding model
try:
return embeddings.distances(word, category)
except:
return [1e16] # If word is not present, return big integer..
def topic(word): # Determine category score for word
topic_list = [embedding_distances(word.lower(), category) for category in
CATEGORIES] # compute distances to categories
topic_list = [min(l) for l in topic_list] # compute average of each sublist
min_value = min(topic_list)
return topic_list.index(min_value), min_value
topic_dists = [[topic(word) for word in l] for l in key_words]
def cluster_topics(topic_dist):
topic_dict = {}
for t in topic_dist:
if t[0] in topic_dict:
topic_dict[t[0]] = (min(topic_dict[t[0]][0], t[1]), topic_dict[t[0]][1] + 1)
else:
topic_dict[t[0]] = (t[1], 1)
topics = [(key, value[0]) for key, value in topic_dict.items()]
values = [x[1] for x in topics]
return topics[values.index(min(values))]
categorized_tweets = [{"text": tweets_raw[i][0], "category": CATEGORY_NAMES[cluster_topics(topic_dists[i])[0]],
"time": str(tweets_raw[i][1])} for i in range(len(topic_dists))]
return categorized_tweets
import os
import glob
import sqlite3
from polyglot.text import Text, Word
from polyglot.downloader import downloader
from polyglot.mapping import Embedding
downloader.download("embeddings2.pt")
downloader.download("pos2.pt")
downloader.download("morph2.pt")
downloader.supported_tasks(lang="pt")
embeddings = Embedding.load(
"/Users/emersonantonio/polyglot_data/embeddings2/pt/embeddings_pkl.tar.bz2"
)
#neighbors = embeddings.nearest_neighbors("verde")
#for w,d in zip(neighbors, embeddings.distances("green", neighbors)):
# print("{:<8}{:.4f}".format(w,d))
# Criar o Banco de Dados
con = sqlite3.connect('./db/dadosDipolNLTK.db')
cur = con.cursor()
sql_create = 'CREATE TABLE IF NOT EXISTS miniDicionario '\
'(' \
' id integer primary key AUTOINCREMENT, '\
' word varchar(50), ' \
' radical varchar(50), ' \
' tag varchar(50)' \
')'
from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
from os import curdir, sep
from word2vec import transform_text, getKthNeighbour, closest_k_points_tsne
from polyglot.mapping import Embedding
import json
from tsne import tsne
from word2vec import transform_text
# from sklearn.manifold import TSNE
PORT_NUMBER = 8080
polish_embeddings = Embedding.load("polyglot-pl.pkl")
# ------------- t-SNE init ------------------------------
# model = TSNE(n_components=2, random_state=0)
# np.set_printoptions(suppress=True)
# tsne_rep = tsne(polish_embeddings.vectors)
# This class will handles any incoming request from
# the browser
print json.dumps(closest_k_points_tsne(polish_embeddings, "Beata", 10))
class myHandler(BaseHTTPRequestHandler):
def do_POST(self):
if self.path == "/sentence-find-near":
def langmodelload(language):
########################
global stop_words
global question_words
global embeddings
global model
global lang_dict
########################
LibLocLang = "./udpipe-ud/"
########################
if language == "en":
model = Model(LibLocLang + 'english-ewt-ud-2.5-191206.udpipe')
elif language == "ar":
model = Model(LibLocLang + 'arabic-padt-ud-2.5-191206.udpipe')
elif language == "zh":
model = Model(LibLocLang + 'chinese-gsdsimp-ud-2.5-191206.udpipe')
elif language == "id":
model = Model(LibLocLang + 'indonesian-gsd-ud-2.5-191206.udpipe')
elif language == "ko":
model = Model(LibLocLang + 'korean-gsd-ud-2.5-191206.udpipe')
elif language == "pt":
model = Model(LibLocLang + 'portuguese-gsd-ud-2.5-191206.udpipe')
elif language == "vi":
model = Model(LibLocLang + 'vietnamese-vtb-ud-2.5-191206.udpipe')
elif language == "hi":
model = Model(LibLocLang + 'hindi-hdtb-ud-2.5-191206.udpipe')
elif language == "jp":
model = Model(LibLocLang + 'japanese-gsd-ud-2.5-191206.udpipe')
elif language == 'es':
model = Model(LibLocLang + 'spanish-gsd-ud-2.5-191206.udpipe')
########################
base_question_words = [
'where', 'which', "who", "why", "what", "when", "please", "how", "is",
"are", "will", "could", "should", "was", "were", "do", "did", "can"
]
question_words = []
for i in range(0, len(base_question_words)):
question_words.append(
Text(base_question_words[i]).transliterate(language))
########################
if stopwords.has_lang(
language
) and language != "hi" and language != "ar" and language != "zh" and language != "vi" and language != "ko" and language != "jp" and language != "id" and language != "ms":
########################
stop_words = list(stopwords.stopwords(language))
stop_words_list = []
########################
for i in range(0, len(stop_words)):
try:
text = Text(stop_words[i], hint_language_code=language)
########################
if (text.pos_tags[0][1] != "NOUN") and (
text.pos_tags[0][1] != "VERB") and (text.pos_tags[0][1]
!= "PRON"):
stop_words_list.append(text.pos_tags[0][0])
except Exception as e:
print(e)
stop_words = stop_words_list
else:
print(language + " has errors.")
stop_words = []
########################
########################
embeddings = Embedding.load("./polyglot_data/embeddings2/" + language +
"/embeddings_pkl.tar.bz2")
lang_dict[language] = {
'model': model,
'embeddings': embeddings,
'stop_words': stop_words
}
from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
from os import curdir, sep
from word2vec import transform_text, getKthNeighbour, closest_k_points_tsne
from polyglot.mapping import Embedding
import json
from tsne import tsne
from word2vec import transform_text
# from sklearn.manifold import TSNE
PORT_NUMBER = 8080
polish_embeddings = Embedding.load("polyglot-pl.pkl")
# ------------- t-SNE init ------------------------------
# model = TSNE(n_components=2, random_state=0)
# np.set_printoptions(suppress=True)
# tsne_rep = tsne(polish_embeddings.vectors)
# This class will handles any incoming request from
# the browser
print json.dumps(closest_k_points_tsne(polish_embeddings, "Beata", 10))
class myHandler(BaseHTTPRequestHandler):
def do_POST(self):
if self.path=="/sentence-find-near":
def embedding(text, embeddingPATH):
embeddings = Embedding.load(embeddingPATH)
neighbors = embeddings.nearest_neighbors(text)
for w, d in zip(neighbors, embeddings.distances(text, neighbors)):
print("{}\n{}".format(w,d))
def load_embedding(self):
path = os.path.join(self.c["data_root"], "embeddings",
self.c["language"] + ".tar.bz2")
return PolyglotEmbedding.load(path)
#This gives only the polyglot embeddings.
import numpy as np
from polyglot.mapping import Embedding
import pickle
from pos_helper import *
from nltk import pos_tag
src_embeddings = Embedding.load(
"/home/krishna/polyglot_data/embeddings2/en/embeddings_pkl.tar.bz2")
tar_embeddings = Embedding.load(
"/home/krishna/polyglot_data/embeddings2/de/embeddings_pkl.tar.bz2")
def make_align_dict(inp, nwords):
inplist = inp.split()
aldict = {}
for j in range(nwords):
aldict[j] = []
for j in inplist:
a, b = j.split('-')
a, b = int(a), int(b)
if b not in aldict:
aldict[b] = []
aldict[b].append(a)
return aldict
def get_target_embedding(ind, inlist):
try:
e2 = tar_embeddings[inlist[ind]]
from polyglot.mapping import Embedding
import processitem
import numpy as np
embeddings = Embedding.load(
'/home/luka/polyglot_data/embeddings2/nl/embeddings_pkl.tar.bz2')
words = []
vectors = []
for w, v in embeddings:
words.append(w)
vectors.append(v)
file = open('./RELPRON/RELPRON/translation_basic.txt', 'r', encoding='latin-1')
items_raw = file.readlines()
file.close()
items_neat = []
for i in items_raw:
neat = processitem.Item(i)
items_neat.append(neat)
#calculate lexical baselines: headN and V vectors
NN_dist = []
NV_dist = []
for i in items_neat:
t = i.termN
h = i.headN
v = i.V
if t in words: