Introduction to Information Retrieval
Christopher D. Manning, Prabhakar Raghavan
Language: English
Pages: 506
ISBN: 0521865719
Format: PDF / Kindle (mobi) / ePub
Class-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.
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Intersection operation to process more complicated queries like: (1.2) (Brutus or Caesar) and not Calpurnia query Query optimization is the process of selecting how to organize the work of anoptimization swering a query so that the least total amount of work needs to be done by the system. A major element of this for Boolean queries is the order in which postings lists are accessed. What is the best order for query processing? Consider a query that is an and of t terms, for instance: (1.3).
Unstructured information. Ad hoc searching over documents has recently conquered the world, powering not only web search engines but the kind of unstructured search that lies behind the large eCommerce web sites. Although the main web search engines differ by emphasizing free text querying, most of the basic issues and technologies 21:26 P1: KRU/IRP irbook CUUS232/Manning 978 0 521 86571 5 16 June 26, 2008 Boolean retrieval of indexing and querying remain the same, as we will see in.
Using Westlaw syntax that would find any of the words professor, teacher, or lecturer in the same sentence as a form of the verb explain. Exercise 1.13 [ ] Try using the Boolean search features on a couple of major web search engines. For instance, choose a word, such as burglar, and submit the queries (i) burglar, (ii) burglar and burglar, and (iii) burglar or burglar. Look at the estimated number of results and top hits. Do they make sense in terms of Boolean logic? Often they haven’t for major.
X for which f reaches its maximum The value of x for which f reaches its minimum Class or category in classification The collection frequency of term t (the total number of times the term appears in the document collection) Set {c 1 , . . . , c J } of all classes A random variable that takes as values members of C Term–document matrix Index of the dth document in the collection D A document Document vector, query vector Set {d1 , . . . , d N } of all documents Set of documents that is in class c.
What is the largest gap you can encode in 1 byte? (iii) What is the largest gap you can encode in 2 bytes? (iv) How many bytes will the postings list in Exercise 5.6 require under this encoding? (Count only space for encoding the sequence of numbers.) Exercise 5.8 [ ] From the following sequence of γ -coded gaps, reconstruct first the gap sequence and then the postings sequence: 1110001110101011111101101111011. Exercise 5.9 γ Codes are relatively inefficient for large numbers (e.g., 1025 in.