By Donald Metzler
Commercial net se's akin to Google, Yahoo, and Bing are used each day by means of hundreds of thousands of individuals around the globe. With their ever-growing refinement and utilization, it has turn into more and more tricky for tutorial researchers to maintain with the gathering sizes and different serious examine matters on the topic of internet seek, which has created a divide among the knowledge retrieval examine being performed inside academia and undefined. Such huge collections pose a brand new set of demanding situations for info retrieval researchers.
In this paintings, Metzler describes powerful info retrieval types for either smaller, classical info units, and bigger net collections. In a shift clear of heuristic, hand-tuned score features and intricate probabilistic versions, he offers feature-based retrieval versions. The Markov random box version he information is going past the normal but ill-suited bag of phrases assumption in methods. First, the version can simply take advantage of a variety of sorts of dependencies that exist among question phrases, putting off the time period independence assumption that regularly accompanies bag of phrases versions. moment, arbitrary textual or non-textual gains can be utilized in the version. As he indicates, combining time period dependencies and arbitrary good points ends up in a truly powerful, strong retrieval version. additionally, he describes numerous extensions, reminiscent of 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 a variety of initiatives and knowledge sets.
A Feature-Centric View of data Retrieval offers graduate scholars, in addition to educational and commercial researchers within the fields of knowledge retrieval and internet seek with a contemporary point of view on info retrieval modeling and net searches.
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Additional resources for A Feature-Centric View of Information Retrieval
The independence semantics are governed by the Markov property. Markov Property. Let G = (V , E) be the undirected graph associated with a Markov random field, then P (vi |vj =i ) = P (vi |vj : (vi , vj ) ∈ E) for every random variable vi associated with a node in V . The Markov Property states that every random variable in the graph is independent of its non-neighbors given observed values for its neighbors. Therefore, different edge configurations impose different independence assumptions. There are several ways to model the joint distribution P (Q, D) using Markov random fields.
2 Modeling Relevance We begin by describing what we seek to model. The four primary variables in most information retrieval systems are users (U ), queries (Q), documents (D), and releD. 1007/978-3-642-22898-8_3, © Springer-Verlag Berlin Heidelberg 2011 23 24 3 Feature-Based Ranking vance (R). We define the event space to be U × Q × D and define relevance, R ∈ R, to be a random variable over U × Q × D. Thus, some relevance value is associated with every user, query, document tuple. Other factors, such as time and context are ignored.
For example, OQD and UQD are empty under the full independence assumption since that would result in a graph where there are no cliques with two or more query term nodes. However, under the sequential dependence assumption, and with a query of length 2 or more, such cliques will exist and OQD and UQD will be non-empty. Next, we consider cliques that only contain query term nodes. These clique sets are defined in an analogous way to those just defined, except the cliques are only made up of query term nodes and do not contain the document node.