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1
Web Search and Text Mining
Lecture 2Adapted from Manning, Raghaven
and Schuetze
2
Outline
For small collections, linear scan, e.g., unix grep
Large collections => indexing Boolean search model Dictionary
3
Terminology
Term Document Collection/Corpus (a body of documents) Index/Inverted index Dictionary/vocabulary/lexicon
4
Term-document incidence matrix
1 if play contains word, 0 otherwise
Antony and Cleopatra Julius Caesar The Tempest Hamlet Othello Macbeth
Antony 1 1 0 0 0 1
Brutus 1 1 0 1 0 0
Caesar 1 1 0 1 1 1
Calpurnia 0 1 0 0 0 0
Cleopatra 1 0 0 0 0 0
mercy 1 0 1 1 1 1
worser 1 0 1 1 1 0
Boolean Query: Brutus AND Caesar but NOT Calpurnia
5
Incidence vectors
So we have a 0/1 vector for each term. To answer query: take the vectors for
Brutus, Caesar and Calpurnia (complemented) bitwise AND.
110100 AND 110111 AND 101111 = 100100.
6
Bigger corpora
Consider N = 1M documents, each with about 1K terms.
Avg 6 bytes/term including spaces/punctuation 6GB of data in the documents.
Say there are m = 500K distinct terms among these.
7
Can’t build the dense matrix
500K x 1M matrix has half-a-trillion 0’s and 1’s.
But it has no more than one billion 1’s. matrix is extremely sparse.
What’s a better representation? We only record the 1 positions.
Why?
8
Indexes of Books
9
Index of the Web
10
Inverted index
For each term T, we must store a list of all documents that contain T.
Do we use an array or a list for this?
Brutus
Calpurnia
Caesar
1 2 3 5 8 13 21 34
2 4 8 16 32 64128
13 16
What happens if the word Caesar is added to document 14?
11
Inverted index
Linked lists generally preferred to arrays Dynamic space allocation Insertion of terms into documents easy Space overhead of pointers
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
12
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.
13
Sequence of (Modified token, Document ID) pairs.
I did enact JuliusCaesar I was killed
i' the Capitol; Brutus killed me.
Doc 1
So let it be withCaesar. The noble
Brutus hath told youCaesar was ambitious
Doc 2
Term Doc #I 1did 1enact 1julius 1caesar 1I 1was 1killed 1i' 1the 1capitol 1brutus 1killed 1me 1so 2let 2it 2be 2with 2caesar 2the 2noble 2brutus 2hath 2told 2you 2
caesar 2was 2ambitious 2
Indexer steps
14
Sort by terms. Term Doc #ambitious 2be 2brutus 1brutus 2capitol 1caesar 1caesar 2caesar 2did 1enact 1hath 1I 1I 1i' 1it 2julius 1killed 1killed 1let 2me 1noble 2so 2the 1the 2told 2you 2was 1was 2with 2
Term Doc #I 1did 1enact 1julius 1caesar 1I 1was 1killed 1i' 1the 1capitol 1brutus 1killed 1me 1so 2let 2it 2be 2with 2caesar 2the 2noble 2brutus 2hath 2told 2you 2caesar 2was 2ambitious 2
Core indexing step.
15
Multiple term entries in a single document are merged.
Frequency information is added.
Term Doc # Term freqambitious 2 1be 2 1brutus 1 1brutus 2 1capitol 1 1caesar 1 1caesar 2 2did 1 1enact 1 1hath 2 1I 1 2i' 1 1it 2 1julius 1 1killed 1 2let 2 1me 1 1noble 2 1so 2 1the 1 1the 2 1told 2 1you 2 1was 1 1was 2 1with 2 1
Term Doc #ambitious 2be 2brutus 1brutus 2capitol 1caesar 1caesar 2caesar 2did 1enact 1hath 1I 1I 1i' 1it 2julius 1killed 1killed 1let 2me 1noble 2so 2the 1the 2told 2you 2was 1was 2with 2
Why frequency?Will discuss later.
16
The result is split into a Dictionary file and a Postings file.
Doc # Freq2 12 11 12 11 11 12 21 11 12 11 21 12 11 11 22 11 12 12 11 12 12 12 11 12 12 1
Term N docs Coll freqambitious 1 1be 1 1brutus 2 2capitol 1 1caesar 2 3did 1 1enact 1 1hath 1 1I 1 2i' 1 1it 1 1julius 1 1killed 1 2let 1 1me 1 1noble 1 1so 1 1the 2 2told 1 1you 1 1was 2 2with 1 1
Term Doc # Freqambitious 2 1be 2 1brutus 1 1brutus 2 1capitol 1 1caesar 1 1caesar 2 2did 1 1enact 1 1hath 2 1I 1 2i' 1 1it 2 1julius 1 1killed 1 2let 2 1me 1 1noble 2 1so 2 1the 1 1the 2 1told 2 1you 2 1was 1 1was 2 1with 2 1
17
The index we just built
How do we process a query? Later - what kinds of queries can we
process?
Today’s focus
18
Query processing: AND
Consider processing the query:Brutus AND Caesar Locate Brutus in the Dictionary;
Retrieve its postings. Locate Caesar in the Dictionary;
Retrieve its postings. “Merge” the two postings:
128
34
2 4 8 16 32 64
1 2 3 5 8 13
21
Brutus
Caesar
19
34
1282 4 8 16 32 64
1 2 3 5 8 13 21
The merge
Walk through the two postings simultaneously, in time linear in the total number of postings entries
128
34
2 4 8 16 32 64
1 2 3 5 8 13 21
Brutus
Caesar2 8
If the list lengths are x and y, the merge takes O(x+y)operations.Crucial: postings sorted by docID.
20
Query optimization
What is the best order for query processing?
Consider a query that is an AND of t terms. For each of the t terms, get its postings,
then AND them together.Brutus
Calpurnia
Caesar
1 2 3 5 8 16 21 34
2 4 8 16 32 64128
13 16
Query: Brutus AND Calpurnia AND Caesar
21
Query optimization example
Process in order of increasing freq: start with smallest set, then keep cutting
further.
Brutus
Calpurnia
Caesar
1 2 3 5 8 13 21 34
2 4 8 16 32 64128
13 16
This is why we keptfreq in dictionary
Execute the query as (Caesar AND Brutus) AND Calpurnia.
22
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, which we will study later in the course.
But these tasks are often done heuristically …
23
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)
24
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?
25
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?
26
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)
Will often index “meta-data” separately Creation date, format, etc.
27
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’
28
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!
29
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, windows Enter: Windows Search: Windows
Potentially more powerful, but less efficient
30
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 standardly have accents, users often may not type them
German: Tuebingen vs. Tübingen Should be equivalent
31
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”?
32
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…
33
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 (lecture 5) 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”
34
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
35
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.
36
Typical rules in Porter
sses ss ies i ational ate tional tion
Weight of word sensitive rules (m>1) EMENT →
replacement → replac cement → cement
37
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
38
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
39
Dictionary entries – first cut
ensemble.french
時間 .japanese
MIT.english
mit.german
guaranteed.english
entries.english
sometimes.english
tokenization.english
These may be grouped by
language (or not…).
More on this in ranking/query
processing.
40
Phrase queries
Want to answer queries such as “stanford university” – as a phrase
Thus the sentence “I went to university at Stanford” is not a match. The concept of phrase queries has proven
easily understood by users; about 10% of web queries are phrase queries
No longer suffices to store only <term : docs> entries
41
A first attempt: Biword indexes
Index every consecutive pair of terms in the text as a phrase
For example the text “Friends, Romans, Countrymen” would generate the biwords friends romans romans countrymen
Each of these biwords is now a dictionary term
Two-word phrase query-processing is now immediate.
42
Longer phrase queries
Longer phrases are processed as we did with wild-cards:
stanford university palo alto can be broken into the Boolean query on biwords:
stanford university AND university palo AND palo alto
Without the docs, we cannot verify that the docs matching the above Boolean query do contain the phrase.
Can have false positives!
43
Issues for biword indexes
False positives, as noted before Index blowup due to bigger dictionary
For extended biword index, parsing longer queries into conjunctions: E.g., the query tangerine trees and
marmalade skies is parsed into tangerine trees AND trees and
marmalade AND marmalade skies
Not standard solution (for all biwords)
44
Solution 2: Positional indexes
Store, for each term, entries of the form:<number of docs containing term;doc1: position1, position2 … ;doc2: position1, position2 … ;etc.>
45
Positional index example
Can compress position values/offsets Nevertheless, this expands postings
storage substantially
<be: 993427;1: 7, 18, 33, 72, 86, 231;2: 3, 149;4: 17, 191, 291, 430, 434;5: 363, 367, …>
Which of docs 1,2,4,5could contain “to be
or not to be”?
46
Processing a phrase query
Extract inverted index entries for each distinct term: to, be, or, not.
Merge their doc:position lists to enumerate all positions with “to be or not to be”. to:
2:1,17,74,222,551; 4:8,16,190,429,433; 7:13,23,191; ...
be: 1:17,19; 4:17,191,291,430,434;
5:14,19,101; ...
Same general method for proximity searches
47
Proximity queries
LIMIT! /3 STATUTE /3 FEDERAL /2 TORT 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?
48
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
49
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