By Victor Lavrenko
A glossy info retrieval approach should have the aptitude to discover, arrange and current very various manifestations of data – equivalent to textual content, photographs, movies or database files – any of that may be of relevance to the person. even though, the idea that of relevance, whereas doubtless intuitive, is admittedly demanding to outline, and it is even more durable to version in a proper way.
Lavrenko doesn't try and bring about a brand new definition of relevance, nor offer arguments as to why any specific definition will be theoretically more suitable or extra whole. in its place, he's taking a generally accredited, albeit a little conservative definition, makes numerous assumptions, and from them develops a brand new probabilistic version that explicitly captures that concept of relevance. With this booklet, he makes significant contributions to the sector 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 strategy, and which explicitly combines files, queries, and relevance in one formalism; moment, a brand new approach for modeling exchangeable sequences of discrete random variables which doesn't make any structural assumptions concerning the info 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 specialise in relevance modeling, rating algorithms, and language modeling.
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Additional info for A Generative Theory of Relevance
All of these models are either directly based on the PRP, or can be closely related to the principle. The major distinction between the models lies in how the authors choose to estimate the probability of relevance P (R = 1|D). 2 The Classical Probabilistic Model We will ﬁrst consider a probabilistic model of retrieval proposed by Robertson and Sparck Jones in . The model was initially named the Binary Independence Model , reﬂecting the basic assumptions it made about occurrences of words in documents.
3), it becomes necessary only when we have no way of observing the relevance variable R. Faced with these diﬃculties, Robertson and Sparck Jones make the following assumptions: 1. pv =qv if v∈Q. When a word is not present in the query, it has an equal probability of occurring in the relevant and non-relevant documents. 3) will only include words that occur both in the document and in the query, all other terms cancel out. 20 2 Relevance 2. 5 if v∈Q. If a word does occur in the query, it is equally likely to be present or absent in a relevant document.
1 An Informal Introduction to the Model 39 along with elaborate textual description of what is pictured in that bitmap. 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.