老鹅校长的技术stack

This paper introduces a machine learning framework based on Lexicalized Hidden Markov Models (HMMs) for extracting and analyzing opinions from web product reviews. The framework aims to identify product-related entities (such as features and components) and classify their associated opinions (positive or negative). By integrating part-of-speech tags and contextual information into the HMMs, the model improves its accuracy in identifying both frequent and infrequent opinion phrases. The method also incorporates a bootstrapping process to self-learn new vocabularies, reducing the need for extensive manual labeling. Experimental results show that this framework outperforms rule-based methods in opinion sentence extraction and opinion polarity classification, providing a more robust solution for opinion mining in e-commerce environments.

#deepLearning/supervisedLearning

监督学习

定义：

监督学习是一种基于已标记数据进行训练的机器学习方法。训练数据由输入特征和对应的目标输出（标签）组成，模型的目标是学习输入与输出之间的映射关系，以便对新数据进行准确的预测。

无监督学习

定义：

无监督学习是在没有预先标记的数据上进行训练的机器学习方法。模型需要自行发现数据的结构、模式或分布，以揭示数据中的隐藏信息。

自监督学习（额外补充）

定义：

自监督学习是一种特殊的无监督学习，模型从数据本身生成标签进行训练，旨在学习数据的有效表示。

KNN 描述