By Donald Metzler
Commercial internet se's corresponding to Google, Yahoo, and Bing are used on a daily basis through thousands of individuals around the globe. With their ever-growing refinement and utilization, it has develop into more and more tricky for educational researchers to take care of with the gathering sizes and different serious learn matters on the topic of internet seek, which has created a divide among the knowledge retrieval learn being performed inside of academia and undefined. Such huge collections pose a brand new set of demanding situations for info retrieval researchers.
In this paintings, Metzler describes powerful details retrieval versions for either smaller, classical facts units, and bigger net collections. In a shift clear of heuristic, hand-tuned score features and complicated probabilistic types, he provides feature-based retrieval types. The Markov random box version he info is going past the conventional but ill-suited bag of phrases assumption in methods. First, the version can simply make the most a variety of forms of dependencies that exist among question phrases, disposing of the time period independence assumption that frequently accompanies bag of phrases types. moment, arbitrary textual or non-textual gains can be utilized in the version. As he exhibits, combining time period dependencies and arbitrary positive aspects leads to a truly powerful, robust retrieval version. furthermore, he describes numerous extensions, resembling an automated function choice set of rules and a question enlargement framework. The ensuing version and extensions offer a versatile framework for powerful retrieval throughout quite a lot of initiatives and knowledge sets.
A Feature-Centric View of knowledge Retrieval presents graduate scholars, in addition to educational and commercial researchers within the fields of knowledge retrieval and net seek with a latest point of view on info retrieval modeling and net searches.
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Additional resources for A Feature-Centric View of Information Retrieval
3 The Markov Random Field Model 25 Fig. 1 Three possible ways to model the joint distribution P (Q, D) using Markov random fields However, this model is too coarsely specified and does not provide any insight into the types of term dependencies that are being modeled since it models whole queries and documents. Option B breaks the query apart into individual terms and treats the document as a whole. Given a query of length n, this results in a graph with n query term nodes and a document node. This option provides more specific control over which query term dependencies are modeled.
Consistently and significantly improves effectiveness over bag of words models across a wide range of tasks and data sets. Another reason we focus on the MRF model is because it has been the focus of a great deal of recent research and has been consistently shown to provide a robust, flexible, and extensible feature-based retrieva framework (Bendersky and Croft 2008; Eguchi 2005; Lang et al. 2010; Lease 2009; Metzler et al. 2004b, 2005b, 2006; Metzler and Croft 2005, 2007; Wang et al. 2010a, 2010b).
15) These examples illustrate that the canonical form allows us to compactly define a large, rich set of MRFs for use with information retrieval tasks. 16) document + query independent document dependent where λc and fc are the parameter and weighting (feature) function associated with clique c, respectively. Given a query Q as evidence, we can use the model to rank documents in descending order according of the conditional P (D|Q). Fortunately, properties of 38 3 Feature-Based Ranking Fig. 3 Illustration showing how the full independence model generalizes unigram language modeling and BM25 (top), and how the sequential dependence model generalizes bigram language modeling (bottom) rankings allow us to significantly simplify the computation.
A Feature-Centric View of Information Retrieval by Donald Metzler