Presentation on theme: "2015-5-11 Clustering Search Results Using PLSA 洪春涛."— Presentation transcript:
Clustering Search Results Using PLSA 洪春涛
Outlines Motivation Introduction to document clustering and PLSA algorithm Working progress and testing results
Motivation Current Internet search engines are giving us too much information Clustering the search results may help find the desired information quickly
The writer Truman Capote The film Truman Capote A demo of the searching result from Google.
Document clustering Put the ‘similar’ documents together => How do we define ‘similar’?
Vector Space Model of documents The Vector Space Model (VSM) sees a document as a vector of terms: Doc1: I see a bright future. Doc2:I see nothing. Iseeabrightfuturenothing doc doc
The distance between doc1 and doc2 is then defined as Cosine as Distance Between Documents
Problems with cosine similarity Synonymy: different words may have the same meaning –Car manufacturer=automobile maker Polysemy: a word may have several different meanings - ‘Truman Capote’ may mean the writer or the film => We need a model that reflects the ‘meaning’
Probabilistic Latent Semantic Analysis Graphical model of PLSA: D1 Z1 W1 D: document Z: latent class W: word These can also be written as: D2 Z1 W D
Through Maximization Likelihood, one gets the estimated parameters: P(d|z) This is what we want – a document-topic matrix that reflects meanings of the documents. P(w|z) P(z)
Our approach 1.Get the P(d|z) matrix by PLSA, and 2.Use k-means clustering algorithm on the matrix
Problems with this approach PLSA takes too much time solution: optimization & parallelization