Topic Models

Topic models is a probabilistic approach to Document Clustering:

  • create a probabilistic generative model for text documents
  • represent corpus as a function of hidden variables


Notation and problem:

  • $D_1, \ ... \ , D_n$ are documents
  • $T_1, \ ... \ , T_k$ are topics (sort of "clusters")
  • each document may belong to several topics - so these "clusters" are Fuzzy
  • probability of $D_i$ belonging to $T_j$ is $P(T_j \mid D_i)$
  • but cluster membership is secondary in this problem
  • the main problem is to find latent topics that generated documents - which is why it's called Topic Modeling
  • let $t_1, \ ... \ , t_d$ be $d$ terms from the lexicon
  • then the probability that $t_l$ occurs in $T_j$ is $P(t_l \mid T_j)$


Thus, we need to estimate the following probabilities:

  • $P(T_j \mid D_i)$ and $P(t_l \mid T_j)$
  • usually parameters are learned via maximum likelihood methods like Expectation Maximization


There are two types of Topic Modeling techniques:


Sources

  • Aggarwal, Charu C., and ChengXiang Zhai. "A survey of text clustering algorithms." Mining Text Data. Springer US, 2012. [1]
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