• 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

面向文摘的中药方剂与疾病关系抽取研究

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()