word_seq.append(char2idx.get("PAD"))
sent_seq.append(word_seq)
X_char_te.append(np.array(sent_seq))
# # true y
y = [[tag2idx[w[2]] for w in s] for s in each_sentences]
# # Padding each sentence to have the same lenght
y = pad_sequences(maxlen=MAX_LEN,
sequences=y,
value=tag2idx["PAD"],
padding='post',
truncating='post')
# # One-Hot encode
y_te = [to_categorical(i, num_classes=n_tags + 1) for i in y] # n_tags+1(PAD)
# # Eval
pred_cat = model.predict([
X_word_te,
np.array(X_char_te).reshape((len(X_char_te), MAX_LEN, max_len_char))
])
pred = np.argmax(pred_cat, axis=-1)
y_te_true = np.argmax(y_te, -1)
# # Convert the index to tag
pred_tag = [[idx2tag[i] for i in row] for row in pred]
y_te_true_tag = [[idx2tag[i] for i in row] for row in y_te_true]
report = flat_classification_report(y_pred=pred_tag, y_true=y_te_true_tag)
print(report)
pos_emb = Embedding(input_dim=len(pos),
output_dim=10,
input_length=max_len)(pos_input)
modified_input = keras.layers.concatenate([word_emb, pos_emb])
model_1 = Bidirectional(
LSTM(units=50, return_sequences=True,
recurrent_dropout=0.1))(modified_input)
model = TimeDistributed(Dense(50, activation="relu"))(
model_1) # a dense layer as suggested by neuralNer
crf = CRF(n_tags) # CRF layer
out = crf(model) # output
model = Model([input, pos_input], out)
model.compile(optimizer="rmsprop",
loss=crf.loss_function,
metrics=[crf.accuracy])
print(model.summary())
history = model.fit([X_tr, X_pos_tr],
np.array(y_tr),
batch_size=32,
epochs=60,
validation_split=0.1,
verbose=1)
#Testing
test_pred = model.predict([X_te, X_pos_te], verbose=1)
idx2tag = {i: w for w, i in tag2idx.items()}
pred_labels = pred2label(test_pred)
test_labels = pred2label(y_te)
print("Recall, Precision and F-score are",
get_recall_precision(test_labels, pred_labels, "Destination"))
model.save("BILSTM+CRF_with_pos_without_embeddings.model")
def test_exist(self, glove, test_data, test_labels):
# get word embeddings
utils = wordUtils.Utils()
if glove:
# use glove
self.words_list, self.embedding_matrix = utils.load_glove()
unword_n = len(self.words_list)
else:
self.words_list, self.embedding_matrix = utils.load_word2vec()
unword_n = len(self.words_list)
# get the training corpus
cr = corpusreader.CorpusReader(test_data, test_labels)
corpus = cr.trainseqs
# get the number of the embedding
for idx in range(len(corpus)):
words = corpus[idx]['tokens']
words_id = []
for i in words:
# get the number of the embedding
try:
# the index of the word in the embedding matrix
index = self.words_list.index(i)
except ValueError:
# use the embedding full of zeros to identify an unknown word
index = unword_n
# the index of the word in the embedding matrix
words_id.append(index)
corpus[idx]['embs'] = words_id
input = Input(shape=(None,))
el = Embedding(len(self.words_list) + 1, 200, weights=[self.embedding_matrix], trainable=False)(input)
bl1 = Bidirectional(LSTM(128, return_sequences=True, recurrent_dropout=0.5, dropout=0.5),
merge_mode="concat",
name="lstm1")(el)
bl2 = Bidirectional(LSTM(64, return_sequences=True, recurrent_dropout=0.5, dropout=0.5),
merge_mode="concat",
name="lstm2")(bl1)
bl3 = Bidirectional(LSTM(64, return_sequences=True, recurrent_dropout=0.5, dropout=0.5),
merge_mode="concat",
name="lstm3")(bl2)
model = TimeDistributed(Dense(50, activation="relu"))(bl3) # a dense layer as suggested by neuralNer
crf = CRF(self.lab_len) # CRF layer
out = crf(model) # output
model = Model(input, out)
model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy])
model.summary()
save_load_utils.load_all_weights(model, 'word_models/words_glove_multiLSTM31.h5')
for doc in corpus:
doc_arr = doc['embs']
p = model.predict(np.array([doc_arr]))
p = np.argmax(p, axis=-1)
position = 0
offsets = defaultdict(list)
counter = 0
# check if there are any mutations identified
# {'O': 0, 'B-E': 1, 'I-E': 2, 'E-E': 3, 'S-E': 4}
B = False
last = 0
for idx in p[0]:
if idx == 1 and last == 1:
counter = counter + 1
offsets[counter].append(position)
B = True
elif idx == 1:
B = True
offsets[counter].append(position)
last = 1
elif idx == 2 and B:
offsets[counter].append(position)
last = 2
elif idx == 3 and B:
offsets[counter].append(position)
last = 3
B = False
counter = counter + 1
elif idx == 4:
offsets[counter].append(position)
counter = counter + 1
last = 4
else:
B = False
position = position + 1
# open file to write
textid = str(doc['textid'])
abstract = open("words-silver/" + textid + ".a1", 'w')
for i in offsets:
word = offsets.get(i)
size = len(word)
if size == 1:
s = word[0] # just one; singleton
abstract.write(str(doc['tokstart'][s]) + "\t")
abstract.write(str(doc['tokend'][s]) + "\t")
abstract.write(str(doc['tokens'][s]) + "\n")
elif size > 1:
s = word[0] # start of token
e = word[-1] # end of token
abstract.write(str(doc['tokstart'][s]) + "\t")
abstract.write(str(doc['tokend'][e]) + "\t")
token = ""
for c in word:
token = token + doc['tokens'][c]
abstract.write(str(token) + "\n")