Author Archives: LiqiangGuo

I am back

我胡汉三又回到wordpress啦 哈哈 多年前,在对学术对科研无比热烈的岁月中,create了word process,之后被GFW成功阻拦在了围墙之内, 现在天天忙于给资本家码代码,一时兴起 也许是为了满足虚荣欲,在google scholar search了一把自己的名字,于是我们又见面了,多年不见,你可还好 word press! 五年已过,我已是另一番模样。

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A method using proximity for pseudo-relevance feedback

Selecting Good Expansion Terms for Pseudo-Relevance Feedback. But I think proximity is too simple,maybe some advanced approach should be studied, for example topic model. RBF is useful in IR,but because of the existence of multi-topics in a document (some may … Continue reading

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Latent Dirichlet Allocation

Latent Dirichlet Allocation Probabilistic Topic Models Latent Dirichlet Allocation2

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Probabilistic Latent Semantic Analysis

Probabilistic Latent Semantic Indexing Unsupervised Learning by Probabilistic Latent Semantic Analysis

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Latent Semantic Analysis

The LSA is unable to explicitly capture multiple senses of a word, nor does it take into account that every word occurrence is typically intended to refer to only one meaning at a time. So the LSA can not address … Continue reading

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Discriminative Model vs. Generative Model

Gerative Model andd Discriminative Model

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SST and ST in Tree Kernel

Making tree kernels practical for natural language learning

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Macro Average Precision VS. Micro Average Precision

Note:When the accounts of the returned dcoument lists belonging to different queries are the same, Macro==Micro. Macro_Micro

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Zipf’s law

Zipf’s Law

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Four Methods to Estimate Query Language in IR

1.Relevance Model 2.Divergence minimizatioon model 3.simple mixture model 4.regularied mixture model A Comparative Study of Methods for Estimating Query Language Models with Pseudo Feedback

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