Information Retrieval Techniques MS(CS) Lecture 5 AIR UNIVERSITY MULTAN CAMPUS
Quick Review • Inverted Index Construction (Exercise) • Query Processing Using Inverted Index • Faster Posting Merges: Skip Pointers • Phrase Queries • Bi-word Index • Extended Bi-Word Index • Positional Index
Sec. 2.4.2 Proximity queries • LIMIT! /3 STATUTE /3 FEDERAL /2 TORT • Again, here, /k means “within k words of”. • Clearly, positional indexes can be used for such queries; biword indexes cannot. • Exercise: Adapt the linear merge of postings to handle proximity queries. Can you make it work for any value of k? • This is a little tricky to do correctly and efficiently • See Figure 2.12 of IIR • There’s likely to be a problem on it!
Proximitysearch • We just saw how to use a positional index for phrase searches. • We can also use it for proximity search. • For example: employment /4 place • Find all documents that contain EMPLOYMENT and PLACE within 4 words of each other. • Employment agencies that place healthcare workers are seeing growthis a hit. • Employment agencies that have learned to adapt now place healthcare workers is not a hit. 4
Proximity search? • Usethepositionalindex • Simplest algorithm: look at cross-product of positions of (i) EMPLOYMENT in document and (ii) PLACE in document • Very inefficient for frequent words, especially stop words • Note that we want to return the actual matching positions, not just a list of documents. • This is important for dynamic summaries etc. 5
Sec. 2.4.2 Positional index size • You can compress position values/offsets: • Nevertheless, a positional index expands postings storage substantially link • Nevertheless, a positional index is now standardly used because of the power and usefulness of phrase and proximity queries … whether used explicitly or implicitly in a ranking retrieval system.
Sec. 2.4.2 Postings Positional postings 1000 1 1 100,000 1 100 Document size Positional index size • Need an entry for each occurrence, not just once per document • Index size depends on average document size • Average web page has <1000 terms • SEC filings, books, even some epic poems … easily 100,000 terms • Consider a term with frequency 0.1% Why?
Sec. 2.4.2 Rules of thumb • A positional index is 2–4 as large as a non-positional index • Positional index size 35–50% of volume of original text • Caveat: all of this holds for “English-like” languages
Sec. 2.4.3 Combination schemes • These two approaches can be profitably combined • For particular phrases (“Michael Jackson”, “Britney Spears”) it is inefficient to keep on merging positional postings lists • Even more so for phrases like “The Who” • Williams et al. (2004) evaluate a more sophisticated mixed indexing scheme • A typical web query mixture was executed in ¼ of the time of using just a positional index • It required 26% more space than having a positional index alone
Inverted Index Construction • Positional index size • Dictionary size • Hardware issues • Large collection requirements analysis
Dictionaries • The dictionary is the data structure for storing the term vocabulary. • Term vocabulary: thedata • Dictionary: the data structure for storing the term vocabulary 12
Dictionary as array of fixed-width entries • For each term, we need to store a couple of items: • documentfrequency • pointertopostingslist • . . . • Assume for the time being that we can store this information in a fixed-lengthentry. • Assume that we store these entries in an array. 13
Dictionary as array of fixed-width entries space needed: 20 bytes 4 bytes 4 bytes How do we look up a query term qiin this array at query time? That is: which data structure do we use to locate the entry (row) in the array where qiis stored? 14
Data structures for looking up term • Two main classes of data structures: hashes and trees • Some IR systems use hashes, some use trees. • Criteria for when to use hashes vs. trees: • Is there a fixed number of terms or will it keep growing? • What are the relative frequencies with which various keys will beaccessed? • How many terms are we likely to have? 15
Hashes • Each vocabulary term is hashed into an integer. • Try toavoidcollisions • At query time, do the following: hash query term, resolve collisions, locate entry in fixed-width array • Pros: Lookup in a hash is faster than lookup in a tree. • Lookup time isconstant. • Cons • no way to find minor variants (resume vs. résumé) • no prefix search (all terms starting with automat) • need to rehash everything periodically if vocabulary keeps growing 16
Trees • Trees solve the prefix problem (find all terms starting with automat). • Simplesttree: binarytree • Search is slightly slower than in hashes: O(logM), where M is the size of the vocabulary. • O(logM) only holds for balanced trees. • Rebalancing binary trees is expensive. • B-trees mitigate the rebalancing problem. • B-tree definition: every internal node has a number of children in the interval [a, b] where a, b are appropriate positive integers, e.g., [2, 4]. 17
Binary tree 18
Ch. 4 Index construction • How do we construct an index? • What strategies can we use with limited main memory?
Sec. 4.1 Hardware basics • Many design decisions in information retrieval are based on the characteristics of hardware • We begin by reviewing hardware basics
Sec. 4.1 Hardware basics • Access to data in memory is much faster than access to data on disk. • Disk seeks: No data is transferred from disk while the disk head is being positioned. • Therefore: Transferring one large chunk of data from disk to memory is faster than transferring many small chunks. • Disk I/O is block-based: Reading and writing of entire blocks (as opposed to smaller chunks). • Block sizes: 8KB to 256 KB.
Sec. 4.1 Hardware basics • Servers used in IR systems now typically have several GB of main memory, sometimes tens of GB. • Available disk space is several (2–3) orders of magnitude larger. • Fault tolerance is very expensive: It’s much cheaper to use many regular machines rather than one fault tolerant machine.
Sec. 4.2 Recall IIR 1 index construction • Documents are parsed to extract words and these are saved with the Document ID. Doc 1 Doc 2 I did enact Julius Caesar I was killed i' the Capitol; Brutus killed me. So let it be with Caesar. The noble Brutus hath told you Caesar was ambitious
Sec. 4.2 After all documents have been parsed, the inverted file is sorted by terms. Key step We focus on this sort step. We have 100M items to sort.
Sec. 4.2 Scaling index construction • In-memory index construction does not scale • Can’t stuff entire collection into memory, sort, then write back • How can we construct an index for very large collections? • Taking into account the hardware constraints we just learned about . . . • Memory, disk, speed, etc.
Sec. 4.2 Sort-based index construction • As we build the index, we parse docs one at a time. • While building the index, we cannot easily exploit compression tricks (you can, but much more complex) • The final postings for any term are incomplete until the end. • At 12 bytes per non-positional postings entry (term, doc, freq), demands a lot of space for large collections. • T = 100,000,000 in the case of RCV1 • So … we can do this in memory in 2009, but typical collections are much larger. E.g., the New York Times provides an index of >150 years of newswire • Thus: We need to store intermediate results on disk.
Sec. 4.2 Sort using disk as “memory”? • Can we use the same index construction algorithm for larger collections, but by using disk instead of memory? • No: Sorting T = 100,000,000 records on disk is too slow – too many disk seeks. • We need an external sorting algorithm.
RCV1 collection • Shakespeare’s collected works are not large enough for demonstrating many of the points in this course. • As an example for applying scalable index construction algorithms, we will use the Reuters RCV1 collection. • English newswire articles sent over the wire in 1995 and 1996 (oneyear). 29
Same algorithmfordisk? • Can we use the same index construction algorithm for larger collections, but by using disk instead of memory? • No: Sorting T = 100,000,000 records on disk is too slow – toomanydiskseeks. • We need an external sorting algorithm. 30
“External” sorting algorithm (using few disk seeks) • We must sort T = 100,000,000 non-positionalpostings. • Each posting has size 12 bytes (4+4+4: termID, docID, documentfrequency). • Define a block to consist of 10,000,000 such postings • We can easily fit that many postings into memory. • We will have 10 such blocks for RCV1. • Basic ideaofalgorithm: • For each block: (i) accumulate postings, (ii) sort in memory, (iii) writetodisk • Then merge the blocks into one long sorted order. 31
BlockedSort-BasedIndexing • Key decision: What is the size of one block? 33
Problem withsort-basedalgorithm • Our assumption was: we can keep the dictionary in memory. • We need the dictionary (which grows dynamically) in order to implement a term to termID mapping. • Actually, we could work with term,docID postings instead of termID,docIDpostings . . . • . . . but then intermediate files become very large. (We would end up with a scalable, but very slow index construction method.) 34
Single-pass in-memoryindexing • Abbreviation: SPIMI • Key idea 1: Generate separate dictionaries for each block – no need to maintain term-termID mapping across blocks. • Key idea 2: Don’t sort. Accumulate postings in postings lists astheyoccur. • With these two ideas we can generate a complete inverted indexforeach block. • These separate indexes can then be merged into one big index. 35
Wildcardqueries • mon*: find all docs containing any term beginning with mon • Easy with B-tree dictionary: retrieve all terms t in the range: mon ≤ t < moo • *mon: find all docs containing any term ending with mon • Maintain an additional tree for terms backwards • Then retrieve all terms t in the range: nom ≤ t < non • Result: A set of terms that are matches for wildcard query • Then retrieve documents that contain any of these terms 37
How to handle * in the middle of a term • Example: m*nchen • We could look up m* and *nchen in the B-tree and intersect thetwotermsets. • Expensive • Alternative: permutermindex • Basic idea: Rotate every wildcard query, so that the * occurs atthe end. • Store each of these rotations in the dictionary, say, in a B-tree 38
Permutermindex • For term HELLO: add hello$, ello$h, llo$he, lo$hel, and o$hell to the B-tree where $ is a special symbol 39
Permutermindex • For HELLO, we’ve stored: hello$, ello$h, llo$he, lo$hel, and o$hell • Queries • For X, look up X$ • For X*, look up X*$ • For *X, look up X$* • For *X*, look up X* • For X*Y, look up Y$X* • Example: For hel*o, look up o$hel* • Permuterm index would better be called a permutermtree. • But permuterm index is the more common name. 41
Processing a lookup in the permuterm index • Rotate query wildcard to the right • Use B-tree lookup as before • Problem: Permuterm more than quadruples the size of the dictionary compared to a regular B-tree. (empirical number) 42
k-gram indexes • More space-efficient than permuterm index • Enumerate all character k-grams (sequence of k characters) occurring in a term • 2-grams arecalledbigrams. • Example: from April is the cruelest month we get the bigrams: $a ap pr riil l$ $i is s$ $t th he e$ $c crruue el le esst t$ $m mo on nt h$ • $ is a special word boundary symbol, as before. • Maintain an inverted index from bigrams to the terms that containthebigram 43
k-gram (bigram, trigram, . . . ) indexes • Note that we now have two different types of inverted indexes • The term-document inverted index for finding documents based on a query consisting of terms • The k-gram index for finding terms based on a query consistingofk-grams 45
Processing wildcarded terms in a bigram index • Query mon* can now be run as: $m AND mo AND on • Gets us all terms with the prefix mon . . . • . . . but also many “false positives” like MOON. • We must postfilter these terms against query. • Surviving terms are then looked up in the term-document invertedindex. • k-gram index vs. permutermindex • k-gram index is more space efficient. • Permuterm index doesn’t require postfiltering. 46
Exercise • Google has very limited support for wildcard queries. • For example, this query doesn’t work very well on Google: [gen* universit*] • Intention: you are looking for the University of Geneva, but don’t know which accents to use for the French words for universityandGeneva. • According to Google search basics, 2010-04-29: “Note that the * operator works only on whole words, not parts of words.” • But this is not entirely true. Try [pythag*] and [m*nchen] • Exercise: Why doesn’t Google fully support wildcard queries? 47
Processing wildcard queries in the term-document index • Problem 1: we must potentially execute a large number of Boolean queries. • Most straightforward semantics: Conjunction of disjunctions • For [gen* universit*]: geneva university OR genevauniversitéOR genèveuniversity OR genèveuniversité OR generaluniversities OR . . . • Very expensive • Problem 2: Users hateto type. • If abbreviated queries like [pyth* theo*] for [pythagoras’ theorem] are allowed, users will use them a lot. • This would significantly increase the cost of answering queries. • Somewhat alleviated by Google Suggest 48
Spellingcorrection • Twoprincipaluses • Correctingdocumentsbeingindexed • Correctinguserqueries • Two different methods for spelling correction • Isolatedwordspellingcorrection • Check each word on its own for misspelling • Will not catch typos resulting in correctly spelled words, e.g., an asteroid that fell form the sky • Context-sensitivespellingcorrection • Look atsurroundingwords • Can correct form/from error above 49
Correctingdocuments • We’re not interested in interactive spelling correction of documents (e.g., MS Word) in this class. • In IR, we use document correction primarily for OCR’eddocuments. (OCR = opticalcharacterrecognition) • The general philosophy in IR is: don’t change the documents. 50