结构化学习
最后发布时间 : 2024-01-28 14:55:09
浏览量 :
Structured learning(结构化学习)是一种机器学习方法,旨在处理具有结构化输出的问题。在结构化学习中,输出不再是单个标量或类别,而是具有丰富结构的对象,如序列、树、图等。
传统的机器学习方法通常处理的是独立和标准化的特征和目标变量,而结构化学习方法考虑了特征之间的依赖关系以及如何将这些特征映射到结构化的输出。
结构化学习的一个常见应用是序列标注任务,如命名实体识别(Named Entity Recognition)或语义角色标注(Semantic Role Labeling)。在这些任务中,输入是一个序列(如句子)而输出是对应于输入序列中各个位置的标签序列。
结构化学习方法通常基于条件随机场(Conditional Random Fields,CRF)或最大熵马尔可夫模型(Maximum Entropy Markov Model,MEMM)等模型。这些模型能够对输入序列和输出标签之间的依赖关系进行建模,并通过训练过程来优化模型参数以最大化预测的结构化输出的准确性。
import numpy as np
from sklearn_crfsuite import CRF
# 准备训练数据
X_train = [
[('I', 'O'), ('love', 'O'), ('structured', 'B'), ('learning', 'I')],
[('CRF', 'B'), ('is', 'O'), ('useful', 'O'), ('for', 'O'), ('sequence', 'B'), ('tagging', 'I')]
]
y_train = [
['O', 'O', 'B', 'I'],
['B', 'O', 'O', 'O', 'B', 'I']
]
# 特征提取函数
def word2features(sent, i):
word = sent[i][0]
features = {
'word': word,
'is_first': i == 0,
'is_last': i == len(sent) - 1
}
return features
def sentence2features(sent):
return [word2features(sent, i) for i in range(len(sent))]
def sentence2labels(sent):
return [label for token, label in sent]
# 特征提取
X_train_features = [sentence2features(sent) for sent in X_train]
y_train_labels = [sentence2labels(sent) for sent in y_train]
# 创建和训练 CRF 模型
crf = CRF()
crf.fit(X_train_features, y_train_labels)
# 准备测试数据
X_test = [('I', 'O'), ('like', 'O'), ('machine', 'B'), ('learning', 'I')]
# 特征提取
X_test_features = sentence2features(X_test)
# 预测序列标签
y_pred_labels = crf.predict_single(X_test_features)
print("预测结果:", y_pred_labels)
结构化学习的例子
- 语音识别(Speech recognition)
- X: speech signal(sequence)->Y:text(sequence)
- 翻译(Transaction)
- X:Chinese (sequence) -> Y: English (squence)
- 语法解析(Syntactic Paring)
- X:sentence -> Y: parsing tree(tree structure)
- 目标检测(Object Detection)
- X: Image -> Y: bounding box
- Summarization
- X: long document -> Y: summary (shor paragraph)
- Retrieval
- X:keyword -> Y: search resurt( a list of webpage)