Document Classification: 7 Pragmatic Approaches for Small Datasets

Document classification in natural language processing is crucial for tasks such as spam filtering and news classification, but it often involves challenges with small datasets. The blog explores various pragmatic approaches to text representation that make classification feasible with limited data. It outlines a typical workflow involving data cleaning, tokenization, and text representation, with a focus on methods like CountVectorizer, TfidfVectorizer, Word2Vec, FastText, and GloVe, each offering unique ways to convert text into numerical data for machine learning models. The article emphasizes the importance of using pre-trained word vectors for better performance on small datasets and highlights the evolution of text representation from simple vectorization techniques to advanced models like FastText and GloVe, which capture word context more effectively. Additionally, it discusses sentence-level operations and the significance of context-aware models like BERT for improved semantic understanding in text classification tasks.

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