Text Classification: From Word Frequency to Pretrained Models 文本分类实验：从词频统计到预训练模型演进

文本分类NLP实验TF-IDF词频统计预训练模型BERTTextCNN深度学习

A comprehensive comparative study tracing the evolution of text classification, evaluating statistical bag-of-words, deep neural networks, and pretrained transformers under reproducible benchmarks. 系统梳理与评测文本分类技术的演进脉络，在统一测试基准下对比词袋统计（TF-IDF / Naive Bayes）、深度语义编码（TextCNN / BiLSTM）与微调预训练模型（BERT / RoBERTa）的表征能力与工程效能。

Text Classification: From TF-IDF to Pretrained Models cover

Baseline 传统基线TF-IDF + NB / SVM

Neural Models 深度神经网络TextCNN & BiLSTM

Pretrained 预训练模型BERT / RoBERTa

Evaluation 核心指标Accuracy / Macro-F1

Reference / 参考信息

Chivalry Studio / NLP Benchmark Labs

Technical Stack / 技术框架

PyTorchTransformersscikit-learnHuggingFaceBERTTextCNNBiLSTMTF-IDFPython

01 / Statistical Baselines 统计基线

Word frequency, sparse representations, and linear decision boundaries. 词频统计、稀疏特征向量与线性分类边界。

Evaluating bag-of-words and TF-IDF representations paired with Naive Bayes, Logistic Regression, and SVMs. Examining sparsity challenges, n-gram limitations, and computational efficiency on raw corpora. 以词袋模型（BoW）与 TF-IDF 稀疏向量为起点，结合朴素贝叶斯、逻辑回归与 SVM 评估传统特征工程的表达瓶颈，分析高维稀疏特征与词序缺失对语义判别的局限。

02 / Deep Sequence Modeling 深度序列建模

Distributed representations and local vs sequential feature extraction. 分布式词向量与局部/时序特征捕获。

Transitioning to dense word embeddings (Word2Vec / GloVe) combined with TextCNN for local n-gram filters and BiLSTM with Attention for long-range temporal dependencies. 引入稠密词嵌入表征，对比卷积神经网络（TextCNN）多尺度卷积核的局部特征提取，与双向长短期记忆网络（BiLSTM + Attention）对语序上下文依赖的建模表现。

03 / Pretrained Transformers 预训练模型

Bidirectional contextual embeddings and transfer learning. 双向注意力上下文表征与迁移学习微调。

Fine-tuning pretrained Transformers (BERT, RoBERTa), leveraging multi-head self-attention to capture nuanced syntactic and semantic shifts across classification domains. 基于预训练语言模型（BERT / RoBERTa）进行下游微调，利用多头自注意力机制捕获深层上下文双向表征，在复杂语境与长距离依赖下取得显著的泛化性能飞跃。

Key Contributions / 核心贡献

Implemented end-to-end benchmark pipelines comparing three generations of NLP models. / 搭建横跨三代文本分类范式的端到端评测流水线。

Analyzed trade-offs between computational overhead, latency, and classification accuracy. / 深入剖析算力开销、推理延迟与分类准确率之间的工程权衡。

Visualized embedding spaces and attention heatmaps across semantic categories. / 对稠密特征空间与自注意力权重进行可视化分析与可解释性探究。

Standardized preprocessing, tokenization, and cross-validation pipelines. / 建立标准化的文本预处理、分词与交叉验证对比基准。