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Difference between Precision and recall

different search techniques to locate and retrieve information and recall and precision in indexing
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WilliamsMcmahon,United States,Professional
Published Date:20-07-2017
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Introduction to Information Retrieval Introduction to Information Retrieval Document ingestion www.ThesisScientist.comIntroduction to Information Retrieval Recall the basic indexing pipeline Documents to Friends, Romans, countrymen. be indexed Tokenizer Token stream Friends Romans Countrymen Linguistic modules friend roman countryman Modified tokens 2 4 Indexer friend 1 2 roman Inverted index 16 13 countryman www.ThesisScientist.comIntroduction to Information Retrieval 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?  (CP1252, UTF-8, …) Each of these is a classification problem, which we will study later in the course. But these tasks are often done heuristically … www.ThesisScientist.comIntroduction to Information Retrieval Sec. 2.1 Complications: Format/language  Documents being indexed can include docs from many different languages  A single index may contain terms from many languages.  Sometimes a document or its components can contain multiple languages/formats  French email with a German pdf attachment.  French email quote clauses from an English-language contract  There are commercial and open source libraries that can handle a lot of this stuff www.ThesisScientist.comIntroduction to Information Retrieval Sec. 2.1 Complications: What is a document? We return from our query “documents” but there are often interesting questions of grain size: What is a unit document?  A file?  An email? (Perhaps one of many in a single mbox file)  What about an email with 5 attachments?  A group of files (e.g., PPT or LaTeX split over HTML pages) www.ThesisScientist.comIntroduction to Information Retrieval Introduction to Information Retrieval Tokens www.ThesisScientist.comIntroduction to Information Retrieval 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? www.ThesisScientist.comIntroduction to Information Retrieval Sec. 2.2.1 Tokenization  Issues in tokenization:  Finland’s capital  Finland AND s? 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? www.ThesisScientist.comIntroduction to Information Retrieval 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: IIR ch. 3)  Will often index “meta-data” separately  Creation date, format, etc. www.ThesisScientist.comIntroduction to Information Retrieval 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 www.ThesisScientist.comIntroduction to Information Retrieval Sec. 2.2.1 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 www.ThesisScientist.comIntroduction to Information Retrieval 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 www.ThesisScientist.comIntroduction to Information Retrieval Introduction to Information Retrieval Terms The things indexed in an IR system www.ThesisScientist.comIntroduction to Information Retrieval 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 (IIR 5) means the space for including stop words in a system is very small  Good query optimization techniques (IIR 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” www.ThesisScientist.comIntroduction to Information Retrieval Sec. 2.2.3 Normalization to terms  We may 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 www.ThesisScientist.comIntroduction to Information Retrieval 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 www.ThesisScientist.comIntroduction to Information Retrieval 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 Is this German “mit”? Morgen will ich in MIT …  Crucial: Need to “normalize” indexed text as well as query terms identically www.ThesisScientist.comIntroduction to Information Retrieval 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…  Longstanding Google example: fixed in 2011…+  Query C.A.T.  1 result is for “cats” (well, Lolcats) not Caterpillar Inc. www.ThesisScientist.comIntroduction to Information Retrieval 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 Search: window, windows  Enter: windows Search: Windows, windows, window  Enter: Windows Search: Windows  Potentially more powerful, but less efficient www.ThesisScientist.comIntroduction to Information Retrieval 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 IIR 3 and IIR 9 www.ThesisScientist.com