Latent Semantic Indexing (LSI) is a mathematical technique used in natural language processing and information retrieval to analyze relationships between terms and documents. It works by identifying patterns in how words co-occur across a collection of texts, allowing it to uncover hidden (or “latent”) semantic structures. LSI reduces the dimensionality of data, grouping words and documents into conceptual topics even if they don’t explicitly share the same vocabulary. For example, LSI might recognize that “car” and “automobile” are related because they appear in similar contexts, even if they never occur together in the same document.
LSI operates by constructing a term-document matrix, where rows represent unique terms and columns represent documents. Each cell contains a value like term frequency (TF) or TF-IDF (term frequency-inverse document frequency) to reflect a word’s importance. This matrix is then decomposed using a mathematical method called Singular Value Decomposition (SVD), which breaks it into three smaller matrices. The key step is truncating these matrices to retain only the most significant dimensions (e.g., 100-300), effectively compressing the data into a lower-dimensional space. In this reduced space, terms and documents are represented as vectors, and their similarity can be measured using cosine similarity. For instance, a search query for “vehicle” might match documents containing “car” if their vectors are close in the LSI space, even if “vehicle” isn’t explicitly mentioned.
LSI is particularly useful for tasks like document retrieval, clustering, and topic modeling. A common application is improving search engines by enabling them to handle synonyms or conceptually related terms. However, LSI has limitations: it requires significant computational resources for large datasets, and the reduced dimensions can be hard to interpret. While newer methods like word embeddings (e.g., Word2Vec) or transformer-based models (e.g., BERT) have surpassed LSI in many cases, LSI remains a foundational approach for understanding semantic relationships. Developers might still use it in scenarios where simplicity and interpretability matter, such as small-scale recommendation systems or analyzing document similarity in constrained domains like academic papers or technical manuals.