Introduction to Information Retrieval¶
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
Cited by¶
7 citations across 7 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Sparse Coding
- The same combinatorial-pool-plus-case-specific-panel logic governs a jury drawn from a large eligible roll and an inverted index activating only the documents containing a query's rare terms.
This sourceDescribes inverted-index construction exploiting that term-document vectors are sparse — a query activates only the documents containing its (often rare) terms, the IR instance of a sparse code.
Supported in partVerified against the work's full text
Manning et al. show the term-document matrix is extremely sparse, motivating the inverted index, but contain no jury analogy or 'combinatorial pool plus panel' principle.
“But the crucial observation is that the matrix is ext remely sparse”
- The same combinatorial-pool-plus-case-specific-panel logic governs a jury drawn from a large eligible roll and an inverted index activating only the documents containing a query's rare terms.
Domain-specific¶
- Postings List
- Christopher Manning, Prabhakar Raghavan, and Hinrich Schütze distinguish the vocabulary, dictionary, posting, postings list, and complete inverted index precisely: a posting records that one term occurred in one document, a postings list gathers the postings for that term, and the index consists of the dictionary plus all lists and supporting data
This sourceSection 4.6 presents this construction - invert the user-document access matrix so that each user has a postings list of the documents they may read, intersected with the search results - along with its maintenance and long-list costs. For the second half of this sentence: section 5.3 stores document-ordered postings as gaps and codes them with variable-byte and gamma codes, and section 2.3 adds skip pointers to speed intersection. The survey clause is Zobel and Moffat (2006).
SupportedVerified against the source
- Christopher Manning, Prabhakar Raghavan, and Hinrich Schütze distinguish the vocabulary, dictionary, posting, postings list, and complete inverted index precisely: a posting records that one term occurred in one document, a postings list gathers the postings for that term, and the index consists of the dictionary plus all lists and supporting data
Mechanisms¶
- Inverse Lookup Query
- Its strength is latency and reuse: when the reverse map is already maintained — an inverted index
This sourceExplains that an inverted index permits direct lookup from a term to its postings list.
- Its strength is latency and reuse: when the reverse map is already maintained — an inverted index
- Inverted-Index Sparse Lookup
- Its failure mode arrives when a unit is nearly ubiquitous: its posting list approaches the whole corpus, the merge prunes nothing, and speed collapses — which is why non-selective units earn little weight, the intuition captured by inverse document frequency.
This sourceExplains that ubiquitous terms have long postings and little discriminating power, which inverse document frequency captures by assigning them low weight.
- Its failure mode arrives when a unit is nearly ubiquitous: its posting list approaches the whole corpus, the merge prunes nothing, and speed collapses — which is why non-selective units earn little weight, the intuition captured by inverse document frequency.
- Lookup Table
- Its classic misuse is reaching for it where recall or ranking is actually required — better served by an Inverted Index
This sourceExplains inverted indexes as an efficient structure for document retrieval and ranked search.
- Its classic misuse is reaching for it where recall or ranking is actually required — better served by an Inverted Index
- Nearest-Neighbor Benchmark
- For every query, each metric retrieves its ten nearest catalog items, and the benchmark compares those against the labeled matches, computing precision-at-ten and recall-at-ten
This sourceDefines precision and recall over top-k ranked result sets, including precision-at-ten and recall-at-ten.
- For every query, each metric retrieves its ten nearest catalog items, and the benchmark compares those against the labeled matches, computing precision-at-ten and recall-at-ten
- Semantic Similarity Index
- Because the matches are approximate, the interface shows a similarity score and links each result to the real incident record, and a reviewer confirms relevance before anything is acted on — the discipline that keeps "close in meaning" from being mistaken for "the same problem."
This sourceExplains approximate ranked retrieval using query-document similarity rather than exact matching.
- Because the matches are approximate, the interface shows a similarity score and links each result to the real incident record, and a reviewer confirms relevance before anything is acted on — the discipline that keeps "close in meaning" from being mistaken for "the same problem."
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 2 of 7 citations: 1 supported, 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Was it audited? Yes. A second, independent pass read the citation against the article text and recorded a verdict.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 4 other ways.
- https://nlp.stanford.edu/IR-book/html/htmledition/irbook.html ×2
- https://doi.org/10.1017/CBO9780511809071 ×1
- https://doi.org/10.1017/cbo9780511809071 ×1
- https://nlp.stanford.edu/IR-book/information-retrieval-book.html ×1
Registry ID ref:2aac364f2eb4 · see in the full table