By Victor Lavrenko
A sleek info retrieval approach should have the aptitude to discover, arrange and current very various manifestations of data – corresponding to textual content, photos, video clips or database documents – any of that may be of relevance to the person. notwithstanding, the idea that of relevance, whereas doubtless intuitive, is admittedly not easy to outline, and it truly is even tougher to version in a proper way.
Lavrenko doesn't try and bring about a brand new definition of relevance, nor supply arguments as to why any specific definition can be theoretically stronger or extra whole. in its place, he is taking a generally authorized, albeit a little conservative definition, makes numerous assumptions, and from them develops a brand new probabilistic version that explicitly captures that proposal of relevance. With this publication, he makes significant contributions to the sector of data retrieval: first, a brand new strategy to examine topical relevance, complementing the 2 dominant versions, i.e., the classical probabilistic version and the language modeling method, and which explicitly combines records, queries, and relevance in one formalism; moment, a brand new process for modeling exchangeable sequences of discrete random variables which doesn't make any structural assumptions in regards to the information and that can additionally deal with infrequent events.
Thus his e-book is of significant curiosity to researchers and graduate scholars in details retrieval who concentrate on relevance modeling, score algorithms, and language modeling.
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Extra resources for A Generative Theory of Relevance
2 Advantages of a common representation We admit, the above description assumes a fairly convoluted process for a simple fact of a few keywords making their way into the search box. On the other hand, we gain several distinct advantages by hypothesizing the process described above. These are: 1. We can deﬁne a common generative model. By assuming that documents and queries originate in the same space, we pave the way for deﬁning a single distribution that can describe both documents and queries.
The above argument should be taken with a grain of salt. We are not suggesting that it is impossible to improve the dependency structure of the language modeling approach. We only claim that no improvement should be expected from direct models of dependence – models where random variables Qi are directly conditioned on some Qj=i . That, however, does not mean that no improvement will result from models that capture dependencies indirectly, perhaps via a hidden variable. For example, Berger and Laﬀerty  proposed to model information retrieval as statistical translation of a document into the query.
Note that in this case, we assume that the documents (images) also contain this textual description, even if in reality we are dealing with a collection of images that are not annotated in any way. Similarly, in a video archive, both documents and queries contain a sequence of bitmap frames, the audio signal, augmented with a complete textual transcript of speech in the audio and a narration, describing the objects and actions in the frames. As another example, consider a question answering scenario: in this case we assume that our representation space consists of pairs that combine a given question with a correct answer.
A Generative Theory of Relevance by Victor Lavrenko