1 / 27

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

Download Presentation

Recuperação de Informação B

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related