NLP Applications:Topic Modeling
Sometimes you want to tag documents with topics: auto-tagging articles, sorting customer support tickets, extracting attributes from product reviews. The standard approaches:
- Supervised, if you have labeled training data. Standard text classification.
- Latent Semantic Analysis (LSA): uses matrix factorization on a term-document matrix to find latent topics.
- Latent Dirichlet Allocation (LDA): a probabilistic model that treats each document as a mixture of topics. The classic unsupervised approach.
- Transformer-based: encode each word and the full document using a transformer; identify the words whose embeddings are closest to the document embedding (via cosine similarity). Those are your topic keywords.
The transformer approach works without any labeled training data, but the keywords it identifies are semantically close to the document, not just frequently occurring. A meaningful upgrade over LSA/LDA for many applications.
Real World: Topic Modeling Is Everywhere
Every customer support system that routes tickets to teams is doing topic modeling. Every news app that auto-tags articles is doing topic modeling. Every product review system that highlights "what people are saying about size, fit, comfort" is doing topic modeling on attributes. This is one of the most common "behind the scenes" NLP tasks in industry.
A legal tech company wants to summarize each page of a 200-page contract as a bullet list of key obligations. They want the summaries to use the exact language from the contract to avoid misrepresentation. Which summarization approach is more appropriate?