Sentence embeddings are crucial for semantic auditing, but choosing the right method can be challenging. This guide covers four main approaches: simple averaging of word vectors, TF-IDF weighted averaging, Smooth Inverse Frequency (SIF), and time-weighted embeddings. Each method has trade-offs in terms of complexity, interpretability, and performance. SIF is recommended for general-purpose semantic tasks due to its ability to remove common noise. Time-weighted embeddings are useful for temporal analysis. The guide also discusses practical considerations like dimensionality reduction and evaluation metrics. For NLP engineers, this provides a solid foundation for building robust semantic auditing systems, applicable to tasks like plagiarism detection, content moderation, and document similarity.
A comprehensive guide comparing sentence embedding techniques—averaging, TF-IDF, SIF, and time-weighted—for semantic auditing and text analysis.