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Chapter 2 Information Retrieval Part-1

Chapter 2 Information Retrieval Part-1. Modern Information Retrieval. Document representation Using keywords Relative weight of keywords Query representation Keywords Relative importance of keywords Retrieval model Similarity between document and query Rank the documents

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Chapter 2 Information Retrieval Part-1

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  1. Chapter 2Information RetrievalPart-1

  2. Modern Information Retrieval • Document representation • Using keywords • Relative weight of keywords • Query representation • Keywords • Relative importance of keywords • Retrieval model • Similarity between document and query • Rank the documents • Performance evaluation of the retrieval process

  3. Document Representation Transforming a text document to a weighted list of keywords

  4. Stopwords Figure 2.2 A partial list of stopwords

  5. Activity: Document Representation Transform the text in the document given into a weighted list of keywords.

  6. Stemming A given word may occur in a variety of syntactic forms • plurals • past tense • gerund forms (a noun derived from a verb) Example The word connect, may appear as • connector, connection, connections, connected, connecting, connects, preconnection, and postconnection.

  7. Stemming A stem is what is left after its affixes (prefixes and suffixes) are removed Suffixes • connector, connection, connections, connected, connecting, connects, Prefixes • preconnection, and postconnection. Stem • connect

  8. Porter’s Algorithm • Letters A, E, I, O, and U are vowels • A consonant in a word is a letter other than A, E, I, O, or U, with the exception of Y • The letter Y is a vowel if it is preceded by a consonant, otherwise it is a consonant • For example, Y in synopsis is a vowel, while in toy, it is a consonant • A consonant in the algorithm description is denoted by c, and a vowel by v

  9. Porter’s algorithmStep 1 Step 1: plurals and past participles

  10. Porter’s algorithmStep 2 Steps 2–4: straightforward stripping of suffixes

  11. Porter’s algorithmStep 3 Steps 2–4: straightforward stripping of suffixes

  12. Porter’s algorithmStep 4 Steps 2–4: straightforward stripping of suffixes

  13. Porter’s algorithmStep 5 Steps 5: tidying-up

  14. Porter’s algorithm Suffix stripping of a vocabulary of 10,000 words (http://www.tartarus.org/~martin/)

  15. For the Tutorial • Bring your laptop/ lab • Make sure you have Java installed • Bring any English language text document, extension must be .txt • Number of words (no more than 1000 words)

  16. Document Representation

  17. Term-Document Matrix • Term-document matrix (TDM) is a two-dimensional representation of a document collection. • Rows of the matrix represent various documents • Columns correspond to various index terms • Values in the matrix can be either the frequency or weight of the index term (identified by the column) in the document (identified by the row).

  18. Term-Document matrix

  19. Sparse Matrixes- triples

  20. Sparse Matrixes- Pairs

  21. Normalization • raw frequency values are not useful for a retrieval model • prefer normalized weights, usually between 0 and 1, for each term in a document • dividing all the keyword frequencies by the largest frequency in the document is a simple method of normalization:

  22. Normalized Term-Document Matrix

  23. Vector Representation of document d1 (word, frequency, normalized frequency)

  24. Mini project (Survey) Arabic language stemmer design • Survey and compare existing Arabic language stemmers and write a research paper. • Design an Arabic Language stemmer Reading: Hints on writing technical reports and papers

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