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Recuperação de Informação B

Recuperação de Informação B. Cap. 06: Text and Multimedia Languages and Properties (Introduction, Metadata and Text) 6.1, 6.2, 6.3 November 01, 1999. Introduction. Text main form of communicating knowledge. Document

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Recuperação de Informação B

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  1. Recuperação de Informação B Cap. 06: Text and Multimedia Languages and Properties (Introduction, Metadata and Text) 6.1, 6.2, 6.3 November 01, 1999

  2. Introduction • Text • main form of communicating knowledge. • Document • loosely defined, denote a single unit of information. • can be any physical unit • a file • an email • a Web Page

  3. Introduction • Document • Syntax and structure • Semantics • Information about itself

  4. Introduction • Document Syntax • Implicit, or expressed in a language (e.g, TeX) • Powerful languages: easier to parse, difficult to convert to other formats. • Open languages are better (interchange) • Semantics of texts in natural language are not easy for a computer to understand • Trend: languages which provides information on structure, format and semantics being readable by human and computers

  5. Introduction • New applications are pushing for format such that information can be represented independetly of style. • Style: defined by the author, but the reader may decide part of it • Style can include treatment of other media

  6. Metadata • “Data about the data” • e.g: in a DBMS, schema specifies name of the relations, attributes, domains, etc. • Descriptive Metadata • Author, source, length • Dublin Core Metadata Element Set • Semantic Metadata • Characterizes the subject matter within the document contents • MEDLINE

  7. Metadata • MARC 100 0020 1 $aHagler, Ronald. 245 0074 14$aThe bibliographic... 250 0012 $a3rd. Ed. 260 0052 $aChicago :$bALA, $c1997

  8. Metadata • Metadata information on Web documents • cataloging, content rating, property rights, digital signatures • New standard: Resource Description Framework • description of Web resources to facilitate automated processing of information • nodes and attched atribute/values pairs • Metadescription of non-textual objects • keyword can be used to search the objects

  9. Metadata • RDF Example <RDF:RDF> <RDF:Description RDF:HREF = “page.html”> <DC:Creator> John Smith </DC:Creator> <DC:Title> John’s Home Page </DC:Title> </RDF:Description> </RDF:RDF>

  10. Metadata • RDF Schema Exemple

  11. Text • Text coding in bits • EBCDIC, ASCII • Initially, 7 bits. Later, 8 bits • Unicode • 16 bits, to accommodate oriental languages

  12. Text • Formats • No single format exists • IR system should retrieve information from different formats • Past: IR systems convert the documents • Today: IR systems use filters

  13. Text • Formats • Formats for document interchange (RTF) • Formats for displaying (PDF, PostScript) • Formats for encode email (MIME) • Compressed files • uuencode/uudecode, binhex

  14. Text • Information Theory • Amount of information is related to the distribution of symbols in the document. • Entropy: • Definition of entropy depends on the probabilities of each symbol. • Text models are used to obtain those probabilites

  15. Text • Example - Entropy • 001001011011

  16. Text • Example - Entropy • 111111111111

  17. Text • Modeling Natural Language • Symbols: separate words or belong to words • Symbols are not uniformly distributed • binomial model • Dependency of previous symbols • k-order markovian model • We can take words as symbols

  18. Text • Modeling Natural Language • Words distribution inside documents • Zipf´s Law: i-th most frequent word appears 1/i times of the most frequent word • Real data fits better with  between 1.5 and 2.0

  19. Text • Modeling Natural Language • Example - word distibution (Zipf’s Law) • V=1000,  = 2 • most frequent word: n=300 • 2nd most frequent: n=76 • 3rd most frequent: n=33 • 4th most frequent: n=19

  20. Text • Modeling Natural Language • Skewed distribution - stopwords • Distribution of words in the documents • binomial distribution • Poisson distribution

  21. Text • Modeling Natural Language • Number of distinct words • Heaps’ Law: • Set of different words is fixed by a constant, but the limit is too high

  22. Text • Modeling Natural Language • Heaps’ Law example • k between 10 and 100,  is less than 1 • example: n=400000,  = 0.5 • K=25, V=15811 • K=35, V=22135

  23. Text • Modeling Natural Language • Length of the words • defines total space needed for vocabulary • Heaps’ Law: length increases logarithmically with text size. • In practice, a finit-state model is used • space has p=0.2 • space cannot apear twice subsequently • there are 26 letters

  24. Text • Similarity Models • Distance Function • Should be symmetric and satisfy triangle inequality • Hamming Distance • number of positions that have different characters reverse receive

  25. Text • Similarity Models • Edit (Levenshtein) Distance • minimum number of operations needed to make strings equal survey surgery • superior for modeling syntatic errors • extensions: weights, transpositions, etc

  26. Text • Similarity Models • Longest Common Subsequence (LCS) survey - surgery LCS: surey • Documents: lines as symbols (diff in Unix) • time consuming • similar lines • Fingerprints • Visual tools

  27. Conclusions • Text is the main form of communicating knowledge. • Documents have syntax, structure and semantics • Metadata: information about data • Formats of text • Modeling Natural Language • Entropy • Distribution of symbols • Similarity

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