By Victor Lavrenko
A smooth details retrieval approach should have the aptitude to discover, arrange and current very diverse manifestations of knowledge – comparable to textual content, photos, movies or database documents – any of that could be of relevance to the consumer. despite the fact that, the idea that of relevance, whereas probably intuitive, is really difficult to outline, and it is even more durable to version in a proper way.
Lavrenko doesn't try to bring on a brand new definition of relevance, nor offer arguments as to why any specific definition may be theoretically better or extra whole. as a substitute, he is taking a greatly authorized, albeit slightly conservative definition, makes a number of assumptions, and from them develops a brand new probabilistic version that explicitly captures that concept of relevance. With this ebook, he makes significant contributions to the sector of knowledge retrieval: first, a brand new option to examine topical relevance, complementing the 2 dominant types, i.e., the classical probabilistic version and the language modeling process, and which explicitly combines files, queries, and relevance in one formalism; moment, a brand new strategy for modeling exchangeable sequences of discrete random variables which doesn't make any structural assumptions in regards to the info and that may additionally deal with infrequent events.
Thus his e-book is of significant curiosity to researchers and graduate scholars in info retrieval who focus on relevance modeling, score algorithms, and language modeling.
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Additional info for A Generative Theory of Relevance
The model was initially named the Binary Independence Model , reﬂecting the basic assumptions it made about occurrences of words in documents. However, since 1976 the model has been re-formulated a number of times to a degree where it can hardly be called “binary” and, as we shall argue later on, the term “independence” is also questionable. The model is also known as the Okapi model , the City model, or simply as the probabilistic model . A very detailed account of the recent state of the model is provided by the original authors in [131, 132].
It certainly does not seem plausible that a rich representation like this could follow the same underlying distribution as a two-word query. If nothing else, the space that embodies the documents appears to be diﬀerent than the space containing the queries. 1 Representation of documents and requests To circumvent the above problem we are going to take a somewhat radical step – we are going to assume that queries and documents originate in an abstract space which is rich enough to represent the attributes of either.
For example, in the case of a text-only collection, the documents remain unchanged, but the query transformation cuts out all the syntactic glue, removes duplicates and discards all but two or three salient keywords. If we are dealing with a collection of images, the transformation would apply to both documents and queries. Documents would be stripped of their textual description, leaving only the bitmap form. For queries, the transform would drop the image portion and reduce the description to a short list of keywords, as we described above.
A Generative Theory of Relevance by Victor Lavrenko