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Full-Text Articles in Computer Sciences
Query-Dependent Selection Of Retrieval Alternatives, Niranjan Balasubramanian
Query-Dependent Selection Of Retrieval Alternatives, Niranjan Balasubramanian
Open Access Dissertations
The main goal of this thesis is to investigate query-dependent selection of retrieval alternatives for Information Retrieval (IR) systems. Retrieval alternatives include choices in representing queries (query representations), and choices in methods used for scoring documents. For example, an IR system can represent a user query without any modification, automatically expand it to include more terms, or reduce it by dropping some terms. The main motivation for this work is that no single query representation or retrieval model performs the best for all queries. This suggests that selecting the best representation or retrieval model for each query can yield improved …
Samplerank: Training Factor Graphs With Atomic Gradients, Michael Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew Mccallum
Samplerank: Training Factor Graphs With Atomic Gradients, Michael Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew Mccallum
Andrew McCallum
We present SampleRank, an alternative to contrastive divergence (CD) for estimating parameters in complex graphical models. SampleRank harnesses a user-provided loss function to distribute stochastic gradients across an MCMC chain. As a result, parameter updates can be computed between arbitrary MCMC states. SampleRank is not only faster than CD, but also achieves better accuracy in practice (up to 23% error reduction on noun-phrase coreference).