A Feature-Centric View of Information Retrieval - download pdf or read online

By Donald Metzler

ISBN-10: 3642228976

ISBN-13: 9783642228971

Commercial internet se's reminiscent of Google, Yahoo, and Bing are used each day through 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 take care of with the gathering sizes and different severe learn matters concerning internet seek, which has created a divide among the data retrieval study 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 info units, and bigger internet collections. In a shift clear of heuristic, hand-tuned rating services and intricate probabilistic versions, he offers feature-based retrieval versions. 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 a variety of different types of dependencies that exist among question phrases, disposing of the time period independence assumption that regularly accompanies bag of phrases types. moment, arbitrary textual or non-textual positive aspects can be utilized in the version. As he indicates, combining time period dependencies and arbitrary gains ends up in a truly strong, strong retrieval version. furthermore, he describes numerous extensions, akin to an automated function choice set of rules and a question growth framework. The ensuing version and extensions supply a versatile framework for powerful retrieval throughout quite a lot of projects and information 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 latest point of view 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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OQD —set of cliques containing the document node and two or more query terms that appear in sequential order within the query. • UQD —set of cliques containing the document node and two or more query terms that appear unordered within the query. Note that the cliques that make up each set may change for different dependence model types. 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.

The advantage of using one of the structures just described is that there is no need to rely on natural language processing techniques, which can often produce noisy output, especially on short segments of text. Of course, some of the dependencies imposed by the structure may be incorrect, but in general, they capture meaningful relationships between terms. 2 Potential Functions In order to compute the MRF’s joint probability mass function (Eq. 1), a set of potential functions must be defined over configurations of the maximal cliques in the underlying graph.

3) ψ(q3 , D) = exp λ3 f3 (q3 , D) + λ4 f4 (q3 , D) . 4) This construction is not unique since the clique {D} is a sub-clique of all three maximal cliques. Therefore, we can assign feature function f5 (D) to any of the maximal cliques. In the previous set of potential functions, it was assigned to the maximal clique {q1 , D}. 6) ψ(q3 , D) = exp λ3 f3 (q3 , D) + λ4 f4 (q3 , D) + λ5 f5 (D) . 7) It is critical to note that, even though the potential function definitions are not guaranteed to be unique using this formulation, the joint probability mass function will be unique.

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A Feature-Centric View of Information Retrieval by Donald Metzler

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