关系抽取
最后发布时间:2024-01-24 21:46:23
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- 在关系抽取领域中,开源的 GATE(general architecture for text engineering)工具软件,较为常用
- 使用监督学习的方式进行关系抽取,需要大量标注的数据,然后经常将问题转换为分类问题,如 SVM 方法等
- 0监督学习的方式需要耗费的人力物力成本较高,因此在半监督学习中如何使用较少的已标注数据完成关系抽取问题,获得越来越多的关注,如基于Bootstrap 的方法
python 句法解析提取特征,构建svm模型预测关系 代码
import nltk
import numpy as np
from sklearn import svm
from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# 句法解析和特征提取
def extract_features(sentence):
# 使用NLTK进行句法解析
parser = nltk.ChartParser(nltk.data.load('grammars/book_grammars/feat0.fcfg'))
for tree in parser.parse(sentence.split()):
# 提取特征
features = {}
for subtree in tree.subtrees():
if subtree.label() == 'S':
continue
# 获取依存关系和关系中的词
relation = subtree.label()
words = ' '.join(subtree.leaves())
features[words] = relation
return features
# 加载训练数据
def load_data():
# 根据实际情况加载训练数据,这里以示例数据为例
sentences = ['John loves Mary', 'Mary hates John']
labels = ['loves', 'hates']
return sentences, labels
# 构建特征向量和标签
def build_features_and_labels(sentences, labels):
features = []
target_labels = []
for sentence, label in zip(sentences, labels):
sentence_features = extract_features(sentence)
features.append(sentence_features)
target_labels.append(label)
return features, target_labels
# 主函数
def main():
# 加载训练数据
sentences, labels = load_data()
# 构建特征向量和标签
features, target_labels = build_features_and_labels(sentences, labels)
# 特征向量转换为数值表示
vectorizer = DictVectorizer(sparse=False)
X = vectorizer.fit_transform(features)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, target_labels, test_size=0.2, random_state=42)
# 构建SVM模型
clf = svm.SVC(kernel='linear')
# 拟合模型
clf.fit(X_train, y_train)
# 预测测试集
y_pred = clf.predict(X_test)
# 输出分类报告
print(classification_report(y_test, y_pred))
if __name__ == '__main__':
main()
python 代码 Word2Vec 训练的词向量,构建的 LSTM 模型
LSTM 是 RNN 的一种变体,旨在解决传统 RNN 面临的长期依赖问题。长期依赖是指在处理长序列时,传统 RNN 很难有效地捕捉到很早之前的信息。LSTM 通过引入门控机制(输入门、遗忘门和输出门)来控制信息的流动,从而有效地记忆和利用先前的信息。这使得 LSTM 在处理长序列数据时更加强大,并被广泛应用于机器翻译、语音识别和文本生成等任务。
import gensim
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense
# 训练Word2Vec词向量
def train_word2vec(sentences, embedding_dim):
# 构建Word2Vec模型
model = gensim.models.Word2Vec(sentences, size=embedding_dim, min_count=1)
# 获取词汇表和词向量
vocabulary = model.wv.vocab
embeddings = np.zeros((len(vocabulary), embedding_dim))
for i, word in enumerate(vocabulary):
embeddings[i] = model.wv[word]
return vocabulary, embeddings
# 构建LSTM模型
def build_lstm_model(embedding_dim, hidden_units):
model = Sequential()
model.add(Embedding(embeddings.shape[0], embedding_dim, input_length=max_sequence_length, weights=[embeddings], trainable=False))
model.add(LSTM(hidden_units))
model.add(Dense(1, activation='sigmoid'))
return model
# 主函数
def main():
# 准备训练数据
sentences = [
['I', 'love', 'this', 'movie'],
['This', 'is', 'great'],
['The', 'plot', 'is', 'boring']
]
labels = [1, 1, 0]
# 设置词向量维度和LSTM隐藏单元数
embedding_dim = 100
hidden_units = 64
# 训练Word2Vec词向量
vocabulary, embeddings = train_word2vec(sentences, embedding_dim)
# 构建LSTM模型
model = build_lstm_model(embedding_dim, hidden_units)
# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# 将训练数据转换为索引序列
tokenizer = tf.keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(sentences)
sequences = tokenizer.texts_to_sequences(sentences)
max_sequence_length = max(len(sequence) for sequence in sequences)
padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen=max_sequence_length)
# 拟合模型
model.fit(padded_sequences, labels, epochs=10, batch_size=1)
# 预测新数据
test_sentences = [
['This', 'is', 'amazing'],
['I', 'dislike', 'it']
]
test_sequences = tokenizer.texts_to_sequences(test_sentences)
padded_test_sequences = tf.keras.preprocessing.sequence.pad_sequences(test_sequences, maxlen=max_sequence_length)
predictions = model.predict_classes(padded_test_sequences)
# 打印预测结果
for sentence, prediction in zip(test_sentences, predictions):
print(sentence, '->', prediction[0])
if __name__ == '__main__':
main()