By Victor Lavrenko
A smooth details retrieval process should have the aptitude to discover, manage and current very various manifestations of data – reminiscent of textual content, photos, video clips or database documents – any of that could be of relevance to the consumer. even if, the concept that of relevance, whereas likely intuitive, is de facto not easy to outline, and it is even tougher to version in a proper way.
Lavrenko doesn't try and bring on a brand new definition of relevance, nor supply arguments as to why any specific definition can be theoretically better or extra whole. as a substitute, he is taking a greatly permitted, albeit a bit of conservative definition, makes numerous assumptions, and from them develops a brand new probabilistic version that explicitly captures that suggestion of relevance. With this publication, he makes significant contributions to the sector of data retrieval: first, a brand new technique to examine topical relevance, complementing the 2 dominant types, i.e., the classical probabilistic version and the language modeling method, 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 may additionally deal with infrequent events.
Thus his publication is of significant curiosity to researchers and graduate scholars in info retrieval who concentrate on relevance modeling, score algorithms, and language modeling.
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Extra resources for A Generative Theory of Relevance
2 It is a personal observation that almost every mathematically inclined graduate student in Information Retrieval attempts to formulate some sort of a nonindependent model of IR within the ﬁrst two to three years of his or her studies. The vast majority of these attempts yield no improvements and remain unpublished. 3 Existing Models of Relevance 23 Unfortunately, empirical evaluations [51, 50] of the new model suggest that by and large it performs no better than the original. When improvements were observed they were mostly attributed to expanding the query with additional words, rather than to a more accurate modeling of probabilities.
After re-arranging the indices v in the products to descend down the tree, we have a way to model relevance without assuming mutual independence. 2 It is a personal observation that almost every mathematically inclined graduate student in Information Retrieval attempts to formulate some sort of a nonindependent model of IR within the ﬁrst two to three years of his or her studies. The vast majority of these attempts yield no improvements and remain unpublished. 3 Existing Models of Relevance 23 Unfortunately, empirical evaluations [51, 50] of the new model suggest that by and large it performs no better than the original.
Non-interactive. We will not be modeling any evolution of user’s information need. Our model explicitly accounts for the fact that a single information need can be expressed in multiple forms, but we do not view these in the context of an interactive search session. 4. Topicality. e. the semantic correspondence between a given request and a given document. We will not be addressing issues of presentation, novelty, or suitability to a particular task. 3 Existing Models of Relevance This book is certainly not the ﬁrst endeavor to treat relevance in probabilistic terms.