PageRank-without-hyperlinks

PageRank-without-hyperlinks - Weights on edges Definition...

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PageRank without hyperlinks: Structural re-ranking using links induced by language models Oren Kurland and Lillian Lee Presentation by Yang Yu
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Related Work ± Query-dependent clustering ± Do not directly induces an obvious ranking of doc. ± Techniques based on graphs ± Similarly used in text summarization
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Main Idea ± Using language model to improve IR performance ± Generation links , which are based on the probability assigned by the language model induced from one document ± Structural re-ranking ± The analogy between hyperlinks and generation links is not perfect. ± Compute an ordering not of the entire corpus but of a set.
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Structural Re-ranking ± Generation Graphs ± pd ( · ) denotes the smoothed unigram language model induced from d ± Example d 1: Toronto Sheffield Salvador d 2: Salvador Salvador Salvador ± Definition 1: The top α generators of a document d D init ± Definition 2: The offspring of a document d D init {o D init : d TopGen ( o ) }
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Structural Re-ranking ± Generation Graphs (cont.)
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Unformatted text preview: Weights on edges Definition 3: Weights on smoothed graph Structural Re-ranking Computing Graph Centrality Uniform Influx Weighted Influx Recursive Uniform Influx Recursive Weighted Influx Structural Re-ranking Incorporating initial scores Uniform Influx + LM Weighted Influx + LM , Recursive Uniform Influx + LM Recursive Weighted Influx + LM . Structural Re-ranking Generation Probabilities: Maximum-likelihood estimate (MLE) of w with respect to x Dirichlet-smoothed version : Structural Re-ranking Generation Probabilities (cont.) Kullback-Leibler divergence D Evaluation 8 8 0 8 8 8 4 6 3 7 4 64/96 7 5 10 7 29/48 U-In+LM > U-In = 10 W-In+Lm > W-In = 8 R-U-In+LM > R-U-In = 11 R-W-In+LM > R-W-In =10 39/48 Evaluation Links based on the vector-space model Evaluation Non-structural re-ranking Questions and Comments...
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PageRank-without-hyperlinks - Weights on edges Definition...

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