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INF 2914 Information Retrieval and Web Search

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1 INF 2914 Information Retrieval and Web Search
Lecture 3: Parsing/Tokenization/Storage These slides are adapted from Stanford’s class CS276 / LING 286 Information Retrieval and Web Mining

2 (Offline) Search Engine Data Flow
Parse & Tokenize Global Analysis Index Build Crawler web page - Scan tokenized web pages, anchor text, etc - Generate text index - Parse - Tokenize - Per page analysis - Dup detection - Static rank comp - Anchor text - … 1 2 3 4 in background tokenized web pages dup table rank table anchor text inverted text index

3 Inverted index 2 4 8 16 32 64 128 Dictionary Brutus Calpurnia Caesar 1
Posting 2 4 8 16 32 64 128 Dictionary Brutus Calpurnia Caesar 1 2 3 5 8 13 21 34 13 16 Postings lists Sorted by docID (more later on why).

4 Inverted index construction
Documents to be indexed. Friends, Romans, countrymen. Tokenizer Token stream. Friends Romans Countrymen Linguistic modules Modified tokens. friend roman countryman Indexer Inverted index. friend roman countryman 2 4 13 16 1

5 Plan for this lecture The Dictionary Storage XML Introduction Parsing
Tokenization What terms do we put in the index? Storage Log structured file systems XML Introduction

6 Parsing a document What format is it in? What language is it in?
pdf/word/excel/html? What language is it in? What character set is in use? Each of these is a classification problem. But these tasks are often done heuristically …

7 Complications: Format/language
Documents being indexed can include docs from many different languages A single index may have to contain terms of several languages. Sometimes a document or its components can contain multiple languages/formats French with a German pdf attachment. What is a unit document? A file? An ? (Perhaps one of many in an mbox.) An with 5 attachments? A group of files (PPT or LaTeX in HTML)

8 Tokenization

9 Tokenization Input: “Friends, Romans and Countrymen” Output: Tokens
Each such token is now a candidate for an index entry, after further processing Described below But what are valid tokens to emit?

10 Tokenization Issues in tokenization: Finland’s capital 
Finland? Finlands? Finland’s? Hewlett-Packard  Hewlett and Packard as two tokens? State-of-the-art: break up hyphenated sequence. co-education ? the hold-him-back-and-drag-him-away-maneuver ? It’s effective to get the user to put in possible hyphens San Francisco: one token or two? How do you decide it is one token?

11 Numbers 3/12/91 Mar. 12, 1991 55 B.C. B-52 My PGP key is 324a3df234cb23e Often, don’t index as text. But often very useful: think about things like looking up error codes/stacktraces on the web (One answer is using n-grams, lectures 6 and 7) Will often index “meta-data” separately Creation date, format, etc.

12 Tokenization: Language issues
L'ensemble  one token or two? L ? L’ ? Le ? Want l’ensemble to match with un ensemble German noun compounds are not segmented Lebensversicherungsgesellschaftsangestellter ‘life insurance company employee’

13 Tokenization: language issues
Chinese and Japanese have no spaces between words: 莎拉波娃现在居住在美国东南部的佛罗里达。 Not always guaranteed a unique tokenization Further complicated in Japanese, with multiple alphabets intermingled Dates/amounts in multiple formats フォーチュン500社は情報不足のため時間あた$500K(約6,000万円) Katakana Hiragana Kanji Romaji End-user can express query entirely in hiragana!

14 Tokenization: language issues
Arabic (or Hebrew) is basically written right to left, but with certain items like numbers written left to right Words are separated, but letter forms within a word form complex ligatures استقلت الجزائر في سنة 1962 بعد 132 عاما من الاحتلال الفرنسي. ← → ← → ← start ‘Algeria achieved its independence in 1962 after 132 years of French occupation.’ With Unicode, the surface presentation is complex, but the stored form is straightforward

15 Normalization Need to “normalize” terms in indexed text as well as query terms into the same form We want to match U.S.A. and USA We most commonly implicitly define equivalence classes of terms e.g., by deleting periods in a term Alternative is to do asymmetric expansion: Enter: window Search: window, windows Enter: windows Search: windows Potentially more powerful, but less efficient Execute queries in parallel or do a second pass over the index

16 Normalization: other languages
Accents: résumé vs. resume. Most important criterion: How are your users like to write their queries for these words? Even in languages that have accents, users often may not type them German: Tuebingen vs. Tübingen Should be equivalent

17 Normalization: other languages
Need to “normalize” indexed text as well as query terms into the same form Character-level alphabet detection and conversion Tokenization not separable from this. Sometimes ambiguous: 7月30日 vs. 7/30 Morgen will ich in MIT … Is this German “mit”?

18 Case folding Reduce all letters to lower case
exception: upper case (in mid-sentence?) e.g., General Motors Fed vs. fed SAIL vs. sail Often best to lower case everything, since users will use lowercase regardless of ‘correct’ capitalization…

19 Stop words With a stop list, you exclude from dictionary entirely the commonest words. Intuition: They have little semantic content: the, a, and, to, be They take a lot of space: ~30% of postings for top 30 But the trend is away from doing this: Good compression techniques means the space for including stopwords in a system is very small Good query optimization techniques mean you pay little at query time for including stop words. You need them for: Phrase queries: “King of Denmark” Various song titles, etc.: “Let it be”, “To be or not to be” “Relational” queries: “flights to London”

20 Thesauri and soundex Handle synonyms and homonyms
Hand-constructed equivalence classes e.g., car = automobile color = colour Rewrite to form equivalence classes Index such equivalences When the document contains automobile, index it under car as well (usually, also vice-versa) Or expand query? When the query contains automobile, look under car as well

21 Soundex Traditional class of heuristics to expand a query into phonetic equivalents Language specific – mainly for names E.g., chebyshev  tchebycheff

22 Lemmatization Reduce inflectional/variant forms to base form E.g.,
am, are, is  be car, cars, car's, cars'  car the boy's cars are different colors  the boy car be different color Lemmatization implies doing “proper” reduction to dictionary headword form

23 Stemming Reduce terms to their “roots” before indexing
“Stemming” suggest crude affix chopping language dependent e.g., automate(s), automatic, automation all reduced to automat. for exampl compress and compress ar both accept as equival to compress for example compressed and compression are both accepted as equivalent to compress.

24 Porter’s algorithm Commonest algorithm for stemming English
Results suggest at least as good as other stemming options Conventions + 5 phases of reductions phases applied sequentially each phase consists of a set of commands sample convention: Of the rules in a compound command, select the one that applies to the longest suffix.

25 Typical rules in Porter
sses  ss ies  i ational  ate tional  tion

26 Other stemmers Other stemmers exist, e.g., Lovins stemmer Single-pass, longest suffix removal (about 250 rules) Motivated by linguistics as well as IR Full morphological analysis – at most modest benefits for retrieval Do stemming and other normalizations help? Often very mixed results: really help recall for some queries but harm precision on others

27 Language-specificity
Many of the above features embody transformations that are Language-specific and Often, application-specific These are “plug-in” addenda to the indexing process Both open source and commercial plug-ins available for handling these

28 Index Build Flow - Overview
Crawled documents Search indexes Per document analysis Global analysis Indexing

29 Per document analysis Multi-format parsing
Handles different files types (HTML, PDF, PowerPoint, etc) Multi-language tokenization, stemming, synonyms, user-defined annotations, etc. Per document analysis is tipically the bottleneck of the index build process 50 times slower than I/O Indexing can be done at I/O speed

30 Incorporating per document analysis
Per document analysis is much slower than indexing Store tokenized documents in a scalable document store Crawled documents Document store Per document analysis

31 Storage

32 Document store Log-structured file system
Only the most recent version of each document is accessible No in place updates Documents are grouped into bundles to optimize I/O Typically built over the file system 3 basic operation modes Document insertion (during per-document analysis) Sequential access for index build Random access during query processing

33 Store design (1/5) Bundle disk layout Header Doc 2 Doc 1
data timestamp # docs hash(URL) offset # fields length field ID field ID Header Doc 2 Doc 1 attributes Bundle disk layout Fixed bundle size (for instance 8MB) All fields are 64-bit aligned All fields are binary Store does not know how to interpret fields Compression Fields are tokens, anchor text, URL, shingle, statistics, etc.

34 Store design (2/5) Document insertion uses a double buffering algorithm and asynchronous I/O Try to fit as many documents as it can in a bundle Schedule write for bundle Start writing the next bundle

35 Store design (3/5) currentBuffer nextBuffer
Store is sequentially scanned during index build and global analysis A double buffering algorithm with asynchronous I/O is also used here Return only the newest version of each document Store is accessed in reverse order LFS semantics currentBuffer nextBuffer Bundle# 1053 Bundle# 1052

36 Store design (4/5) Store cleanup algorithm
“Smarter” algorithms can be used if we are not I/O bound Avoid seeks New documents bundle Bloom filter D1’ Storei+1 D5’ 1 set 1 bundle D6 D1’ 1 bundle copy D5’ Storei D6 D3 1 probe D4 bundle 1 D2 * D1 bundle D3 D4 * D5 D2 * garbage collected

37 Bloom Filters (1/2) Compact data structures for a probabilistic representation of a set Appropriate to answer membership queries False positives!

38 Bloom Filters (2/2) Query for b: check the bits at positions H1(b), H2(b), ..., H4(b).

39 Store design (5/5) During runtime the summarizer uses the store to fetch the tokens (random access) Store provides an API call for retrieving a set of documents (e.g. 20) given its bundle number and offset in the file Internally the store uses a buffer pool for documents Asynchronous I/O is used for exploiting parallelism from the storage subsystem Summarizer releases the documents after it is done

40 Storage Issues Performance Fault tolerance Distribution Redundancy
Field compression Google File System tries to address these issues

41 XML Introduction

42 Preliminaries: XML <conference> <name> PODS </name>
<speaker> <name> Josifovski </name> <paper_cnt> 1 </paper_cnt> </speaker> <name> Fagin </name> <paper_cnt> 3 </paper_cnt> </conference> root x0 conference x1 name x2 x6 PODS x3 speaker speaker x8 x4 x5 x7 name paper_cnt explain the scenario: names/ conferences etc. paper_cnt name Josifovski 3 1 Fagin

43 Preliminaries: XPath 1.0 Result: { x7 }
/conference[name = PODS]/speaker[paper_cnt > 1]/name Query Document root root x0 conference conference x1 name x2 name explain more, what is $, what is slash, $ -> root expand names, put PODS, etc take out the arrow, remove predicates remove slashes – only something else remove animation two possible corresp. Only one matches speaker = PODS PODS x6 x3 speaker speaker x8 paper_cnt > 1 x4 x5 x7 name name paper_cnt paper_cnt name Josifovski 3 1 Fagin Result: { x7 }

44 XML Indexing article section title figure caption “XML”
//title contains(‘Query Processing’) AND //figure//caption contains(‘XML’)] “Query Processing” In an index-based method, 8 tags and text elements need to be verified to process this query (lessons 6 and 7)

45 Position Encoding A1 B1 B2 C1 D1 B3 C2 R (0,7,0) (1,5,1) (2,2,2)
Scheme #1: Begin/End/Level Begin: preorder position of tag/text End: preorder position of last descendent Level: depth Containment: X contains Y iff X.begin < Y.begin <= X.end (assuming well-formed) A1 B1 B2 C1 D1 B3 C2 R (0,7,0) (1,5,1) (2,2,2) (4,4,3) (5,5,3) (6,7,1) (7,7,2) (3,5,2)

46 Position Encoding A1 B1 B2 C1 D1 B3 C2 R (1) (1.1) (1.1.1) (
Scheme #2: Dewey Position of element E = {position of parent}.n, where E is the nth child of its parent Containment: X contains Y iff X is a prefix of Y A1 B1 B2 C1 D1 B3 C2 R (1) (1.1) (1.1.1) ( ) ( ) (1.2) (1.2.1) (1.1.2)

47 Position Encoding Begin/End/Level Dewey Typically more compact
Fewer implementation issues Dewey Encodes positions of all ancestors

48 Path Index Path ID /R 1 /R/A 2 /R/A/B 3 /R/A/B/C 4 /R/A/B/D 5 /R/B 6
/R/B/C 7 A1 B1 B2 C1 D1 B3 C2 R Describe how we actually use path index to speed up processing later… Maybe point out the obvious usage, e.g., for path queries Point out how every position (e.g., B2) is associated with a single path id (e.g., 3) Mention tradeoffs? Cons: Space of path table AND postings, time to process index Pros: enables path twig join and virtual iterators, which can have enormous benefits Path Pattern -> Set of matching path IDs /R/B -> {6} //R//C -> {4, 7}

49 Basic Access Path Inverted posting lists
Posting: <Token, Location> Token = <Term/Tag> Location = <DocumentID, Position> Exercise: Create the posting list representation for the following XML document R A1 B3 B1 B2 C2 C1 D1

50 Inverted index 2 4 8 16 32 64 128 Dictionary Brutus Calpurnia Caesar 1
Posting 2 4 8 16 32 64 128 Dictionary Brutus Calpurnia Caesar 1 2 3 5 8 13 21 34 13 16 Postings lists Sorted by docID (Why on lessons 6/7).

51 Joins in XML Structural (Containment) Joins Twig Joins A || B B || C B
D A || B C D A || B C Many xml queries are branching expressions, therefore efficiently processing twig joins is an important aspect of any xml query processor

52 Resources for today’s lecture
IIR 2 Porter’s stemmer: Rosenblum, Mendel and Ousterhout, John K. (February 1992). "The Design and Implementation of a Log-Structured Filesystem." ACM Transactions on Computer Systems. 10(1) XML Introduction (IIR 10)

53 Trabalho 4 - Proposta Google File System
Map Reduce

54 Trabalho 5 - Proposta XML Parsing, Tokenization, and Indexing
JuruXML - an XML retrieval system at INEX'02 Optimizing cursor movement in holistic twig joins. CIKM 2005:

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