# DL_4_NER
Using Bi-LSTM model for NER in English.
语料库train.txt的前15行：
```
played on Monday ( home team in CAPS ) :
VBD IN NNP ( NN NN IN NNP ) :
O O O O O O O O O O
American League
NNP NNP
B-MISC I-MISC
Cleveland 2 DETROIT 1
NNP CD NNP CD
B-ORG O B-ORG O
BALTIMORE 12 Oakland 11 ( 10 innings )
VB CD NNP CD ( CD NN )
B-ORG O B-ORG O O O O O
TORONTO 5 Minnesota 3
TO CD NNP CD
B-ORG O B-ORG O
......
```
使用Keras创建Bi-LSTM模型的示意图如下：
该模型在原始数据的训练集上的准确率在99%以上，在测试集上的准确率在95%以上。
对新的数据集进行测试：
自己想的三个句子：
输入为：
```
sent = 'James is a world famous actor, whose home is in London.'
```
输出结果为：
```
['James', 'is', 'a', 'world', 'famous', 'actor', ',', 'whose', 'home', 'is', 'in', 'London', '.']
['B-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O']
NER识别结果：
PERSON: James
LOCATION: London
```
输入为：
```
sent = 'Oxford is in England, Jack is from here.'
```
输出为：
```
['Oxford', 'is', 'in', 'England', ',', 'Jack', 'is', 'from', 'here', '.']
['B-PER', 'O', 'O', 'B-LOC', 'O', 'B-PER', 'O', 'O', 'O', 'O']
NER识别结果：
PERSON: Oxford
LOCATION: England
PERSON: Jack
```
输入为：
```
sent = 'I love Shanghai.'
```
输出为：
```
['I', 'love', 'Shanghai', '.']
['O', 'O', 'B-LOC', 'O']
NER识别结果：
LOCATION: Shanghai
```
在上面的例子中，只有Oxford的识别效果不理想，模型将它识别为PERSON，其实应该是ORGANIZATION。
  接下来是三个来自CNN和wikipedia的句子：
输入为：
```
sent = "the US runs the risk of a military defeat by China or Russia"
```
输出为：
```
['the', 'US', 'runs', 'the', 'risk', 'of', 'a', 'military', 'defeat', 'by', 'China', 'or', 'Russia']
['O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC']
NER识别结果：
LOCATION: US
LOCATION: China
LOCATION: Russia
```
输入为：
```
sent = "Home to the headquarters of the United Nations, New York is an important center for international diplomacy."
```
输出为：
```
['Home', 'to', 'the', 'headquarters', 'of', 'the', 'United', 'Nations', ',', 'New', 'York', 'is', 'an', 'important', 'center', 'for', 'international', 'diplomacy', '.']
['O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'I-ORG', 'O', 'B-LOC', 'I-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
NER识别结果：
ORGANIZATION: United Nations
LOCATION: New York
```
输入为：
```
sent = "The United States is a founding member of the United Nations, World Bank, International Monetary Fund."
```
输出为:
```
['The', 'United', 'States', 'is', 'a', 'founding', 'member', 'of', 'the', 'United', 'Nations', ',', 'World', 'Bank', ',', 'International', 'Monetary', 'Fund', '.']
['O', 'B-LOC', 'I-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'I-ORG', 'O', 'B-ORG', 'I-ORG', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O']
NER识别结果：
LOCATION: United States
ORGANIZATION: United Nations
ORGANIZATION: World Bank
ORGANIZATION: International Monetary Fund
```
  这三个例子识别全部正确。
DL_4_NER-master.zip （15个子文件） 
DL_4_NER-master NERSystem 
word_dictionary.pk 470KB output_dictionary.pk 143B
LSTM_model.png 9KB inverse_word_dictionary.pk 470KB label_dictionary.pk 143B 句子长度及出现频数统计图.png 20KB 句子长度累积分布函数图.png 26KB
train.txt 2.21MB DL_4_NER utils.py 1KB Bi_LSTM_Model_training.py 5KB Bi_LSTM_Model_predict.py 2KB data_processing.py 3KB __pycache__ utils.cpython-35.pyc 2KB data_processing.cpython-35.pyc 4KB README.md 3KB资源评论
