1 / 20

Information Retrieval

Information Retrieval. February 3, 2003. Handout #2. Course Information. Instructor: Dragomir R. Radev (radev@si.umich.edu) Office: 3080, West Hall Connector Phone: (734) 615-5225 Office hours: M&F 11-12 Course page: http://tangra.si.umich.edu/~radev/650/

kanoa
Download Presentation

Information Retrieval

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. Information Retrieval February 3, 2003 Handout #2

  2. Course Information • Instructor: Dragomir R. Radev (radev@si.umich.edu) • Office: 3080, West Hall Connector • Phone: (734) 615-5225 • Office hours: M&F 11-12 • Course page: http://tangra.si.umich.edu/~radev/650/ • Class meets on Mondays, 1-4 PM in 409 West Hall

  3. Queries and documents

  4. Queries • Single-word queries • Context queries • Phrases • Proximity • Boolean queries • Natural Language queries

  5. Pattern matching • Words, prefixes, suffixes, substrings, ranges, regular expressions • Structured queries (e.g., XML)

  6. Relevance feedback • Query expansion • Term reweighting • Pseudo-relevance feedback • Latent semantic indexing • Distributional clustering

  7. Document processing • Lexical analysis • Stopword elimination • Stemming • Index term identification • Thesauri

  8. Porter’s algorithm • 1. The measure, m, of a stem is a function of sequences of vowels followed by a consonant. If V is a sequence of vowels and C is a sequence of consonants, then m is: C(VC)mVwhere the initial C and final V are optional and m is the number of VC repeats. m=0 free, why m=1 frees, whose m=2 prologue, compute2. *<X> - stem ends with letter X3. *v* - stem ends in a vowel4. *d - stem ends in double consonant5. *o - stem ends with consonant-vowel-consonant sequence where the final consonant is now w, x, or y

  9. Porter’s algorithm • Suffix conditions take the form current_suffix = = patternActions are in the form old_suffix -> new_suffixRules are divided into steps to define the order of applying the rules. The following are some examples of the rules:STEP CONDITION SUFFIX REPLACEMENT EXAMPLE1a NULL sses ss stresses->stress1b *v* ing NULL making->mak1b1 NULL at ate inflat(ed)->inflate1c *v* y I happy->happi2 m>0 aliti al formaliti->formal3 m>0 icate ic duplicate->duplic4 m>1 able NULL adjustable->adjust5a m>1 e NULL inflate->inflat5b m>1 and NULL single letter controll->control

  10. Porter’s algorithm Example: the word “duplicatable” duplicat rule 4duplicate rule 1b1duplic rule 3 The application of another rule in step 4, removing “ic,” cannotbe applied since one rule from each step is allowed to be applied.

  11. Porter’s algorithm

  12. Relevance feedback • Automatic • Manual • Method: identifying feedback terms Q’ = a1Q + a2R - a3N Often a1 = 1, a2 = 1/|R| and a3 = 1/|N|

  13. Example • Q = “safety minivans” • D1 = “car safety minivans tests injury statistics” - relevant • D2 = “liability tests safety” - relevant • D3 = “car passengers injury reviews” - non-relevant • R = ? • S = ? • Q’ = ?

  14. Automatic query expansion • Thesaurus-based expansion • Distributional similarity-based expansion

  15. WordNet and DistSim wn reason -hypen - hypernyms wn reason -synsn - synsets wn reason -simsn - synonyms wn reason -over - overview of senses wn reason -famln - familiarity/polysemy wn reason -grepn - compound nouns /clair3/tools/relatedwords/relate reason

  16. Related (substitutable) words Wordnet Book: publication, product, fact, dramatic composition, record Computer: machine, expert, calculator, reckoner, figurer Fruit: reproductive structure, consequence, product, bear Politician: leader, schemer Newspaper: press, publisher, product, paper, newsprint Distributional clustering: Book: autobiography, essay, biography, memoirs, novels Computer:adobe, computing, computers, developed, hardware Fruit: leafy, canned, fruits, flowers, grapes Politician: activist, campaigner, politicians, intellectuals, journalist Newspaper: daily, globe, newspapers, newsday, paper

  17. Indexing and searching

  18. Computing term salience • Term frequency (IDF) • Document frequency (DF) • Inverse document frequency (IDF)

  19. Scripts to compute tf and idf cd /clair4/class/ir-w03/hw2 ./tf.pl 053.txt | sort -nr +1 | more ./tfs.pl 053.txt | sort -nr +1 | more ./stem.pl reasonableness ./build-idf.pl ./idf.pl | sort -n +2 | more

  20. Applications of TFIDF • Cosine similarity • Indexing • Clustering

More Related