Learning latent topics – Goals and approaches

Topic modeling discovers hidden themes that capture semantic information beyond individual words in a body of documents. It aims to address a key challenge for a machine learning algorithm that learns from text data by transcending the lexical level of "what actually has been written" to the semantic level of "what was intended." The resulting topics can be used to annotate documents based on their association with various topics.

In practical terms, topic models automatically summarize large collections of documents to facilitate organization and management as well as search and recommendations. At the same time, it enables the understanding of documents to the extent that humans can interpret the descriptions of topics.

Topic models also mitigate the curse of dimensionality that often plagues the BOW model; representing documents with high-dimensional, sparse vectors can make similarity measures noisy...