By Donald Metzler

ISBN-10: 3642228976

ISBN-13: 9783642228971

Commercial internet se's comparable to Google, Yahoo, and Bing are used each day via hundreds of thousands of individuals around the globe. With their ever-growing refinement and utilization, it has turn into more and more tricky for educational researchers to maintain with the gathering sizes and different severe study concerns concerning net 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 internet collections. In a shift clear of heuristic, hand-tuned rating capabilities and intricate probabilistic types, he offers feature-based retrieval types. The Markov random box version he info is going past the normal but ill-suited bag of phrases assumption in methods. First, the version can simply make the most quite a few 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 good points ends up in a really strong, robust retrieval version. moreover, he describes a number of extensions, comparable to an automated characteristic 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 projects and information sets.

A Feature-Centric View of data Retrieval presents graduate scholars, in addition to educational and commercial researchers within the fields of knowledge retrieval and internet seek with a latest standpoint on info retrieval modeling and internet searches.

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Extra resources for A Feature-Centric View of Information Retrieval

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Qi+k ). This rewards documents for preserving the order that the query terms occur in. In the unordered clique set case, we match terms using the Indri unordered window operator (#uwN ), where N defines the maximum size of the window that the terms may occur (ordered or unordered) in. For clique {qi , . . , qj , D} that contains k query terms, documents are matched according to #uwN k(qi . . qj ). Notice that we multiply the number of terms in the clique set by N . If N = 1, then all k query terms must occur, ordered or unordered, within a window of k terms of each other within the document.

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.

For ordered term cliques, we match terms in documents using the Indri ordered window operator (#M), where the parameter M determines how many non-matching terms are allowed to appear between matched terms (Metzler and Croft 2004). For clique {qi , . . , qi+k , D}, we match documents according to #M( qi . . qi+k ). This rewards documents for preserving the order that the query terms occur in. In the unordered clique set case, we match terms using the Indri unordered window operator (#uwN ), where N defines the maximum size of the window that the terms may occur (ordered or unordered) in.

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A Feature-Centric View of Information Retrieval by Donald Metzler

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