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Get A Generative Theory of Relevance PDF

By Victor Lavrenko

ISBN-10: 3540893636

ISBN-13: 9783540893639

ISBN-10: 3540893644

ISBN-13: 9783540893646

A smooth info retrieval procedure should have the aptitude to discover, set up and current very diverse manifestations of knowledge – resembling textual content, photos, video clips or database documents – any of that may be of relevance to the person. although, the concept that of relevance, whereas possible intuitive, is absolutely tough to outline, and it truly is even tougher to version in a proper way.

Lavrenko doesn't try to bring on a brand new definition of relevance, nor supply arguments as to why any specific definition may be theoretically more suitable or extra whole. as a substitute, he is taking a commonly accredited, albeit a little bit conservative definition, makes a number of assumptions, and from them develops a brand new probabilistic version that explicitly captures that idea of relevance. With this e-book, he makes significant contributions to the sphere of knowledge retrieval: first, a brand new technique to examine topical relevance, complementing the 2 dominant versions, i.e., the classical probabilistic version and the language modeling procedure, and which explicitly combines files, queries, and relevance in one formalism; moment, a brand new procedure for modeling exchangeable sequences of discrete random variables which doesn't make any structural assumptions in regards to the facts and that could additionally deal with infrequent events.

Thus his booklet is of significant curiosity to researchers and graduate scholars in info retrieval who focus on relevance modeling, rating algorithms, and language modeling.

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Extra resources for A Generative Theory of Relevance

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In addition, a large body of language modeling publications in these fields serves as a gold-mine of estimation techniques that can be applied in IR – anything from n-gram 30 2 Relevance and cache models in ASR, to translation models in MT, to grammars in NLP. 3. Independence in the language modeling framework Just like the classical probabilistic model, the language modeling approach relies on making a very strong assumption of independence between individual words. However, the meaning of this assumption is quite different in the two frameworks.

Why dependency models fail It is natural to wonder why this is the case – the classical model contains an obviously incorrect assumption about the language, and yet most attempts to relax that assumption produce no consistent improvements whatsoever. In this section we will present a possible explanation. We are going to argue that the classical Binary Independence Model really does not assume word independence, and consequently that there is no benefit in trying to relax the non-existent assumption.

To be specific, in the vector representation we have half a million random variables, each with two possible values: absent or present. ), but that variable can take half a million possible values. Which of these representations is more suitable for Information Retrieval is a very interesting open question. Our feeling is that vector representation might be more natural. 2 we might wonder if dependencies are of any use at all). On the other hand, the sequence representation makes it more natural to model word proximity, phrases and other surface characteristics of text.

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A Generative Theory of Relevance by Victor Lavrenko

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