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:computer: 英文命名实体识别(NER)的研究
Project README
ner-english
:leopard: 英文命名实体识别(NER)的研究
准备
- 数据集：Kaggle-https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/4
- 词汇量：去重之后：
35178 - 句子：
47959 - 实体标签含义：
geo = Geographical Entity 地名
org = Organization 组织
per = Person 人物
gpe = Geopolitical Entity 地理政治
tim = Time indicator 时间
art = Artifact 艺术
eve = Event 时间
nat = Natural Phenomenon 自然现象
模型
-
01_basline
简单的标签统计特征
precision recall f1-score support B-art 0.20 0.05 0.09 402 B-eve 0.54 0.25 0.34 308 B-geo 0.78 0.85 0.81 37644 B-gpe 0.94 0.93 0.94 15870 B-nat 0.42 0.28 0.33 201 B-org 0.67 0.49 0.56 20143 B-per 0.78 0.65 0.71 16990 B-tim 0.87 0.77 0.82 20333 I-art 0.04 0.01 0.01 297 I-eve 0.39 0.12 0.18 253 I-geo 0.73 0.58 0.65 7414 I-gpe 0.62 0.45 0.52 198 I-nat 0.00 0.00 0.00 51 I-org 0.69 0.53 0.60 16784 I-per 0.73 0.65 0.69 17251 I-tim 0.58 0.13 0.21 6528 O 0.97 0.99 0.98 887908 avg / total 0.94 0.95 0.94 1048575 -
02_random_forest_classifier:
基本特征：
首字母是否大写，是否小写，是否为大写，单词长度，是否为数字，是否全为字母上下文特征:
上下文单词的标签以及词性特征方法：
RandomForestClassifierprecision recall f1-score support B-art 0.19 0.08 0.11 402 B-eve 0.39 0.25 0.30 308 B-geo 0.81 0.85 0.83 37644 B-gpe 0.98 0.93 0.95 15870 B-nat 0.28 0.28 0.28 201 B-org 0.71 0.60 0.65 20143 B-per 0.84 0.73 0.78 16990 B-tim 0.90 0.79 0.84 20333 I-art 0.05 0.02 0.02 297 I-eve 0.21 0.10 0.13 253 I-geo 0.74 0.64 0.69 7414 I-gpe 0.80 0.45 0.58 198 I-nat 0.40 0.20 0.26 51 I-org 0.69 0.65 0.67 16784 I-per 0.81 0.74 0.78 17251 I-tim 0.76 0.47 0.58 6528 O 0.98 0.99 0.99 887908 avg / total 0.95 0.96 0.95 1048575 -
03_CRF 条件随机场
特征基本同上
crf=CRF(algorithm='lbfgs', c1=0.1, c2=0.1, max_iterations=100, all_possible_transitions=False)训练结果:
python 03_conditional_random_fields.py --action trainprecision recall f1-score support B-art 0.37 0.11 0.17 402 B-eve 0.52 0.35 0.42 308 B-geo 0.85 0.90 0.88 37644 B-gpe 0.97 0.94 0.95 15870 B-nat 0.66 0.37 0.47 201 B-org 0.78 0.72 0.75 20143 B-per 0.84 0.81 0.82 16990 B-tim 0.93 0.88 0.90 20333 I-art 0.11 0.03 0.04 297 I-eve 0.34 0.21 0.26 253 I-geo 0.82 0.79 0.80 7414 I-gpe 0.92 0.55 0.69 198 I-nat 0.61 0.27 0.38 51 I-org 0.81 0.79 0.80 16784 I-per 0.84 0.89 0.87 17251 I-tim 0.83 0.76 0.80 6528 O 0.99 0.99 0.99 887908 avg / total 0.97 0.97 0.97 1048575测试结果
python 03_conditional_random_fields.py --action testWord ||True ||Pred ============================== Helicopter : O O gunships : O O Saturday : B-tim B-tim pounded : O O militant : O O hideouts : O O in : O O the : O O Orakzai : B-geo B-geo tribal : O O region : O O , : O O where : O O many : O O Taliban : B-org B-org militants : O O are : O O believed : O O to : O O have : O O fled : O O to : O O avoid : O O an : O O earlier : O O military : O O offensive : O O in : O O nearby : O O South : B-geo B-geo Waziristan : I-geo I-geo . : O O -
04_Bi-LSTM
句子长度统计：
通过上图观察，句子最大长度max_len设置为50
训练集和测试集：
X_train:(43163, 50) X_test(4796,50) y_train(43163,50,17) y_test(4796,50,17)model:
input=Input(shape=(max_len,)) model=Embedding(input_dim=n_words,output_dim=50,input_length=max_len)(input) model=Dropout(0.1)(model) model=Bidirectional(LSTM(units=100,return_sequences=True,recurrent_dropout=0.1))(model) out=TimeDistributed(Dense(n_tags,activation='softmax'))(model) # softmax output layer model=Model(input,out) model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])训练结果:
python 04_bilstm.py --action trainEpoch 1/5 38846/38846 [==============================] - 90s 2ms/step - loss: 0.1410 - acc: 0.9643 - val_loss: 0.0622 - val_acc: 0.9818 Epoch 2/5 38846/38846 [==============================] - 88s 2ms/step - loss: 0.0550 - acc: 0.9838 - val_loss: 0.0517 - val_acc: 0.9849 Epoch 3/5 38846/38846 [==============================] - 88s 2ms/step - loss: 0.0459 - acc: 0.9865 - val_loss: 0.0477 - val_acc: 0.9860 Epoch 4/5 38846/38846 [==============================] - 89s 2ms/step - loss: 0.0413 - acc: 0.9878 - val_loss: 0.0459 - val_acc: 0.9865 Epoch 5/5 38846/38846 [==============================] - 89s 2ms/step - loss: 0.0385 - acc: 0.9885 - val_loss: 0.0444 - val_acc: 0.9868
测试结果:
python 04_bilstm.py --action testWord ||True ||Pred ============================== The : O O French : B-gpe B-gpe news : O O agency : O O , : O O Agence : B-org O France : I-org B-geo Presse : I-org I-geo , : O O says : O O one : O O of : O O its : O O photographers : O O has : O O been : O O kidnapped : O O in : O O the : O O Gaza : B-geo B-geo Strip : I-geo I-geo . : O O -
05_Bi-LSTM+CRF
model:
input = Input(shape=(max_len,)) model = Embedding(input_dim=n_words + 1, output_dim=20, input_length=max_len, mask_zero=True)(input) # 20-dim embedding model = Bidirectional(LSTM(units=50, return_sequences=True, recurrent_dropout=0.1))(model) # variational biLSTM model = TimeDistributed(Dense(50, activation="relu"))(model) # a dense layer as suggested by neuralNer crf = CRF(n_tags) # CRF layer out = crf(model) # output model = Model(input, out)训练结果:
python 05_bilstm_crf.py --action trainTrain on 38846 samples, validate on 4317 samples Epoch 1/5 38846/38846 [==============================] - 137s 4ms/step - loss: 0.1651 - acc: 0.9546 - val_loss: 0.0691 - val_acc: 0.9766 Epoch 2/5 38846/38846 [==============================] - 136s 4ms/step - loss: 0.0513 - acc: 0.9815 - val_loss: 0.0429 - val_acc: 0.9834 Epoch 3/5 38846/38846 [==============================] - 131s 3ms/step - loss: 0.0365 - acc: 0.9855 - val_loss: 0.0376 - val_acc: 0.9849 Epoch 4/5 38846/38846 [==============================] - 132s 3ms/step - loss: 0.0315 - acc: 0.9871 - val_loss: 0.0344 - val_acc: 0.9859 Epoch 5/5 38846/38846 [==============================] - 131s 3ms/step - loss: 0.0287 - acc: 0.9879 - val_loss: 0.0339 - val_acc: 0.9857
测试结果:
python 05_bilstm_crf.py --action testWord ||True ||Pred ============================== His : O O schedule : O O includes : O O talks : O O with : O O King : B-per B-per Juan : I-per I-per Carlos : I-per I-per and : O O Spanish : B-gpe B-gpe Prime : B-per B-per Minister : I-per I-per Jose : I-per I-per Luis : I-per I-per Rodriguez : I-per I-per Zapatero : I-per I-per . : O O
演示
The U.S. military in Iraq has sent a team of forensic experts to the northern city of Mosul to investigate the cause of Tuesday 's massive explosion at an American military base that killed 22 people and wounded 72 others .
资料
Open Source Agenda is not affiliated with "Ner English" Project. README Source: yanqiangmiffy/ner-english
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