1 inf 2914 information retrieval and web search lecture 3: parsing/tokenization/storage these slides...
TRANSCRIPT
1
INF 2914Information 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- Per page analysis
tokenizedweb pages
duptable
Parse & Tokenize Global Analysis
2
invertedtext index
1
Crawler
web page - Scan tokenized web pages, anchor text, etc- Generate text index
Index Build
- Dup detection- Static rank comp- Anchor text
- …
3 4
ranktable
anchortext
in background
3
Inverted index
Brutus
Calpurnia
Caesar
2 4 8 16 32 64 128
2 3 5 8 13 21 34
13 16
1
Dictionary Postings lists
Sorted by docID (more later on why).
Posting
4
Inverted index construction
Tokenizer
Token stream. Friends Romans Countrymen
Linguistic modules
Modified tokens. friend roman countryman
Indexer
Inverted index.
friend
roman
countryman
2 4
2
13 16
1
Documents tobe indexed.
Friends, Romans, countrymen.
5
Plan for this lecture
The Dictionary 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? 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 email with a German pdf attachment.
What is a unit document? A file? An email? (Perhaps one of many in an
mbox.) An email with 5 attachments? A group of files (PPT or LaTeX in HTML)
8
Tokenization
9
Tokenization
Input: “Friends, Romans and Countrymen”
Output: Tokens Friends Romans Countrymen
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 100.2.86.144
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
سنة في الجزائر من 132بعد 1962استقلت عاماالفرنسي .االحتالل
← → ← → ← 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 example compressed and compression are both accepted as equivalent to compress.
for exampl compress andcompress ar both acceptas equival 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 http://www.comp.lancs.ac.uk/computing/research/stemming/general/lovins.htm
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
Crawleddocuments
Indexing Searchindexes
Per document analysis
Global analysis
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
Crawleddocuments
Per document analysis Document store
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
data
timestamp
# docs
hash(URL) offset
timestamphash(URL) offset
# fields
lengthfield ID
lengthfield ID
lengthfield ID
data
data
data
# fields
offsetfield ID
offset
offset
offset
length
Header
Doc 2
Doc 1
# fields
# fields
# docs attributes
attributes
attributes
Store design (1/5)
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
Bundle# 1053 Bundle# 1052
currentBuffer nextBuffer
Store design (3/5)
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
36
Store design (4/5) Store cleanup algorithm “Smarter” algorithms can be used if we are not I/O bound
Avoid seeks
D1’
D5’
D6
New documentsbundle
D1
D3
D4
bundle
D5
D2
bundle
Storei
Storei+1
D1’
D5’
D6
bundle
D3
D4
D2
bundle
* garbage collected
*
*
1
1
0
1
0
1
0
1
0
Bloom filter
probe
set
copy
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)Bloom Filters (2/2)
1
1
1
1
Element a
H1(a) = P1
H2(a) = P2
H3(a) = P3
H4(a) = P4
m bits
Bit vector v
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>
<speaker> <name> Fagin </name> <paper_cnt> 3 </paper_cnt> </speaker></conference>
conference
name
speaker
namepaper_cnt
root
speaker
namepaper_cnt
PODS
JosifovskiFagin1 3
x0
x1
x2
x3
x6
x4 x5 x7
x8
43
Preliminaries: XPath 1.0
/conference[name = PODS]/speaker[paper_cnt > 1]/name
conference
name
root
DocumentQuery
Result: { x7 }
speaker
namepaper_cnt
= PODS
> 1
conference
name
speaker
namepaper_cnt
root
speaker
namepaper_cnt
PODS
JosifovskiFagin1 3
x0
x1
x2
x3
x6
x4 x5 x7
x8
44
XML Indexing
//article//section[//title contains(‘Query Processing’) AND
//figure//caption contains(‘XML’)]
In an index-based method, 8 tags and text elements need to be verified to process this query (lessons 6 and 7)
“Query Processing”
article
section
title figure
caption
“XML”
45
Position Encoding
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
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)
(1.1.2.1) (1.1.2.2)
(1.2)
(1.2.1)(1.1.2)
47
Position Encoding
Begin/End/Level Typically more compact Fewer implementation issues
Dewey Encodes positions of all ancestors
48
Path Index
A1
B1 B2
C1 D1
B3
C2
RPath 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
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
A1
B1 B2
C1 D1
B3
C2
R
50
Inverted index
Brutus
Calpurnia
Caesar
2 4 8 16 32 64 128
2 3 5 8 13 21 34
13 16
1
Dictionary Postings lists
Sorted by docID (Why on lessons 6/7).
Posting
51
Joins in XML Structural (Containment) Joins
Twig Joins
A||B
A||B
|| ||C D
B||C
B||D
A||B||C
52
Resources for today’s lecture
IIR 2 Porter’s stemmer:
http://www.tartarus.org/~martin/PorterStemmer/ Rosenblum, Mendel and Ousterhout, John K. (February 1992).
"The Design and Implementation of a Log-Structured Filesystem." ACM Transactions on Computer Systems. 10(1). 26-52
XML Introduction (IIR 10)
53
Trabalho 4 - Proposta
Google File System http://labs.google.com/papers/gfs.html
Map Reduce http://labs.google.com/papers/mapreduce.html
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: 784-791