程序代写代做代考 algorithm information retrieval html Excel COMP6714: Information Retrieval & Web Search

COMP6714: Information Retrieval & Web Search
Introduction to
Information Retrieval
Lecture 2: Preprocessing
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COMP6714: Information Retrieval & Web Search
Ch. 1
Recap of the previous lecture ▪ Basic inverted indexes:
▪ Structure: Dictionary and Postings
▪ Key step in construction: Sorting ▪ Boolean query processing
▪ Intersection by linear time “merging” ▪ Optimizations
▪ Positional index
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COMP6714: Information Retrieval & Web Search
Plan for this lecture Elaborate basic indexing
▪ Preprocessing to form the term vocabulary ▪ Documents
▪ Tokenization
▪ What terms do we put in the index?
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COMP6714: Information Retrieval & Web Search
Recall the basic indexing pipeline
Documents to be indexed.
Token stream.
Modified tokens.
Inverted index.
Tokenizer
Linguistic modules
Indexer
Friends, Romans, countrymen.
Friends
Romans
Countrymen
friend
roman
countryman
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1
4
2
friend
roman
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416
countryman

COMP6714: Information Retrieval & Web Search
Sec. 2.1
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 …
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COMP6714: Information Retrieval & Web Search
Sec. 2.1
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 as HTML pages)
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COMP6714: Information Retrieval & Web Search
TOKENS AND TERMS
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.1
Tokenization
▪ Input: “Friends, Romans and Countrymen”
▪ Output: Tokens ▪ Friends
▪ Romans
▪ Countrymen
▪ A token is an instance of a sequence of characters
▪ Each such token is now a candidate for an index
entry, after further processing ▪ Described below
▪ But what are valid tokens to emit?
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.1
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
▪ lowercase, lower-case, lower case ?
▪ It can be effective to get the user to put in possible hyphens
▪ San Francisco: one token or two? ▪ How do you decide it is one token?
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.1
Numbers
▪ 3/20/91 Mar. 12, 1991 20/3/91 ▪ 55 B.C.
▪ B-52
▪ My PGP key is 324a3df234cb23e
▪ (800) 234-2333
▪ Often have embedded spaces
▪ Older IR systems may not index numbers
▪ But often very useful: think about things like looking up error
codes/stacktraces on the web
▪ (One answer is using n-grams: Lecture 3)
▪ Will often index “meta-data” separately ▪ Creation date, format, etc.
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.1
Tokenization: language issues ▪ French
▪ L’ensembleone token or two? ▪ L ? L’ ? Le ?
▪ Want l’ensemble to match with un ensemble ▪ Until at least 2003, it didn’t on Google
▪ Internationalization!
▪ German noun compounds are not segmented
▪ Lebensversicherungsgesellschaftsangestellter
▪ ‘life insurance company employee’
▪ German retrieval systems benefit greatly from a compound splitter module
▪ Can give a 15% performance boost for German
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.1
南京市长江大桥 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!
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.1
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
▪ ←→ ←→ ←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
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.2
Stop words
▪ With a stop list, you exclude from the dictionary entirely the commonest words. Intuition:
▪ They have little semantic content: the, a, and, to, be
▪ There are a lot of them: ~30% of postings for top 30 words
▪ 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 (lecture 7) 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” vs. “flights from London”
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.3
Normalization to terms
▪ We need to “normalize” words in indexed text as well as query words into the same form
▪ We want to match U.S.A. and USA
▪ Result is terms: a term is a (normalized) word type,
which is an entry in our IR system dictionary
▪ We most commonly implicitly define equivalence classes of terms by, e.g.,
▪ deleting periods to form a term ▪ U.S.A., USA  USA
▪ deleting hyphens to form a term
▪ anti-discriminatory, antidiscriminatory  antidiscriminatory
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.3
Normalization: other languages ▪ Accents: e.g., French résumé vs. resume.
▪ Umlauts: e.g., German: Tuebingen vs. Tübingen ▪ Should be equivalent
▪ 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
▪ Often best to normalize to a de-accented term ▪ Tuebingen, Tübingen, Tubingen  Tubingen
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.3
Normalization: other languages ▪ Normalization of things like date forms
▪ 7月30日 vs. 7/30
▪ Japanese use of kana vs. Chinese characters
▪ Tokenization and normalization may depend on the language and so is intertwined with language detection
Morgen will ich in …
▪ Crucial: Need to “normalize” indexed text as well as query terms into the same form
MIT
Is this German “mit”?
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.3
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…
▪ Google example: ▪ Query C.A.T.
▪ #1 result is for “cat” (well, Lolcats) not Caterpillar Inc.
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.3
Normalization to terms
▪ An alternative to equivalence classing is to do asymmetric expansion
▪ An example of where this may be useful
▪ Enter: window ▪ Enter: windows ▪ Enter: Windows
Search: window, windows
Search: Windows, windows, window Search: Windows
▪ Potentially more powerful, but less efficient
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COMP6714: Information Retrieval & Web Search
Thesauri and soundex
▪ Do we handle synonyms and homonyms?
▪ E.g., by hand-constructed equivalence classes ▪ car = automobile color = colour
▪ We can rewrite to form equivalence-class terms
▪ When the document contains automobile, index it under car-
automobile (and vice-versa) ▪ Or we can expand a query
▪ When the query contains automobile, look under car as well ▪ What about spelling mistakes?
▪ One approach is soundex, which forms equivalence classes of words based on phonetic heuristics
▪ More in lectures 3 and 9
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.4
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
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.4
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.
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.4
Porter’s algorithm
▪ Commonest algorithm for stemming English
▪ Results suggest it’s 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.
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.4
Typical rules in Porter
▪ ssesss ▪iesi
▪ ationalate ▪ tionaltion
▪ Weight of word sensitive rules ▪ (m>1) EMENT →
▪ replacement → replac ▪cement →cement
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.4
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)
▪ Full morphological analysis – at most modest benefits for retrieval
▪ Do stemming and other normalizations help?
▪ English: very mixed results. Helps recall for some queries but harms precision on others
▪ E.g., operative (dentistry) ⇒ oper
▪ Definitely useful for Spanish, German, Finnish, … ▪ 30% performance gains for Finnish!
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COMP6714: Information Retrieval & Web Search
Sec. 2.2.4
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 are available for handling these
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COMP6714: Information Retrieval & Web Search
Sec. 2.2
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.
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COMP6714: Information Retrieval & Web Search
Resources for today’s lecture
▪ IIR 2
▪ MG 3.6, 4.3; MIR 7.2
▪ Porter’s stemmer: http://www.tartarus.org/~martin/PorterStemmer/
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