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Introduction Information Retrieval Indian Statistical Institute Information Retrieval (ISI) Introduction 1 / 20 Course details Books [MRS] Introduction to Information Retrieval , Manning, Raghavan, Schtze. https://nlp.stanford.edu/IR-book/

  1. Introduction Information Retrieval Indian Statistical Institute Information Retrieval (ISI) Introduction 1 / 20

  2. Course details Books [MRS] Introduction to Information Retrieval , Manning, Raghavan, Schütze. https://nlp.stanford.edu/IR-book/ [BCC] Information Retrieval Implementing and Evaluating Search Engines , Büttcher, Clarke, Cormack. http://www.ir.uwaterloo.ca/book/ [CMS] Search Engines: Information Retrieval in Practice , Croft, Metzler, Strohman. http://www.search-engines-book.com/ Foundations and Trends in Information Retrieval (FTIR) https://www.nowpublishers.com/INR Weightage: Mid-sem 20% Project 30% End-sem 50% Slides: Available from http://www.isical.ac.in/~mandar/courses.html and http://www.isical.ac.in/~debapriyo Information Retrieval (ISI) Introduction 2 / 20

  3. Terminology Problem definition: Given a user’s information need , find documents satisfying that need. Information need: what user is looking for Query: actual representation of above Document: any unit / item that can be retrieved For this course, we will only consider textual information (no images/graphics, maps, speech, video, etc.). Information Retrieval (ISI) Introduction 3 / 20

  4. Overview INDEXING Document Index collection Results Retrieval QUERYING engine Information Retrieval (ISI) Introduction 4 / 20

  5. Steps 1. Document acquisition: how is the document collection obtained / constructed? ( LATER ) 2. Indexing: representing documents so that retrieval is easy 3. Retrieval: matching the user query against documents in the collection 4. Evaluation: how to determine whether the system did well? ( NEXT WEEK ) Information Retrieval (ISI) Introduction 5 / 20

  6. Bag of words approach Indexing: document → list of keywords / content-descriptors / terms user’s information need → (natural-language) query → list of keywords Retrieval: measure overlap between query and documents. Information Retrieval (ISI) Introduction 6 / 20

  7. Indexing 1. Tokenisation 2. Stopword removal 3. Stemming 4. Phrase identification 5. Named entity extraction Information Retrieval (ISI) Introduction 7 / 20

  8. Indexing – I Tokenisation: identify individual words. Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on full-text or other content-based indexing. ⇓ Information retrieval IR is the activity of obtaining . . . Information Retrieval (ISI) Introduction 8 / 20

  9. Indexing – II Stopword removal: eliminate common words . . . Information retrieval IR is the activity of obtaining Information Retrieval (ISI) Introduction 9 / 20

  10. Indexing – III Stemming: reduce words to a common root. e.g. resignation, resigned, resigns → resign for common languages, use standard algorithms (Porter). Information Retrieval (ISI) Introduction 10 / 20

  11. Indexing – IV Phrases: multi-word terms e.g. computer science, data mining. Syntactic/linguistic methods use a part of speech tagger look for particular POS sequences, e.g., NN NN, JJ NN Example: computer/NN science/NN Information Retrieval (ISI) Introduction 11 / 20

  12. Indexing – IV Statistical methods: f ( a,b ) > θ (threshold) Raw frequency: f raw ( a, b ) = n ( a,b ) Dice coefficient: f dice ( a, b ) = 2 × n ( a,b ) / ( n a + n b ) n a , n b number of bi-grams whose first (second) word is a ( b ) . . . Information Retrieval (ISI) Introduction 12 / 20

  13. Indexing Document collection → Term-Document Matrix Vocabulary : set of all t 1 t 2 . . . t M words in collection D 1 D 2 N × M binary . Document collection (0-1) matrix . . D N Information Retrieval (ISI) Introduction 13 / 20

  14. Retrieval models Information Retrieval (ISI) Introduction 14 / 20

  15. Boolean model Keywords combined using AND , OR , ( AND ) NOT e.g. (medicine OR treatment) AND (hypertension OR “high blood pressure”) Information Retrieval (ISI) Introduction 15 / 20

  16. Boolean model Keywords combined using AND , OR , ( AND ) NOT e.g. (medicine OR treatment) AND (hypertension OR “high blood pressure”) Efficient and easy to implement (list merging) AND ≡ intersection OR ≡ union Example: medicine → D 1 , D 4 , D 5 , D 10 , . . . hypertension → D 2 , D 4 , D 8 , D 10 , . . . Information Retrieval (ISI) Introduction 15 / 20

  17. Boolean model Keywords combined using AND , OR , ( AND ) NOT e.g. (medicine OR treatment) AND (hypertension OR “high blood pressure”) Efficient and easy to implement (list merging) AND ≡ intersection OR ≡ union Example: medicine → D 1 , D 4 , D 5 , D 10 , . . . hypertension → D 2 , D 4 , D 8 , D 10 , . . . Drawbacks OR — one match as good as many AND — one miss as bad as all no ranking queries may be difficult to formulate Information Retrieval (ISI) Introduction 15 / 20

  18. Vector space model (VSM) Any text item (“document”) is represented as list of terms and associated weights. t 1 t 2 . . . t M D 1 w 11 w 12 w 1 M D 2 w 21 w 22 w 2 M . . . D N w N 1 w N 2 w NM Term = keywords or content-descriptors Weight = measure of the importance of a term in representing the information contained in the document Information Retrieval (ISI) Introduction 16 / 20

  19. Term weights Term frequency (tf) repeated words are strongly related to content importance does not grow linearly with frequency ⇒ use sub-linear function examples: tf ( ) 1 + log( tf ) , 1 + log 1 + log( tf ) , k + tf Inverse document frequency (idf): uncommon term is more important Example: medicine vs. antibiotic commonly used functions N log N − df + 0 . 5 log 1 + df , df + 0 . 5 Information Retrieval (ISI) Introduction 17 / 20

  20. Term weights Normalisation by document length: term-weights for long documents should be reduced long docs. contain many distinct words. long docs. contain same word many times. Intuition: each term covers a smaller portion of the overall information content of a long document use # bytes, # distinct words, Euclidean length, etc. Weight = tf x idf / normalisation Information Retrieval (ISI) Introduction 18 / 20

  21. Term weights: “traditional” weighting schemes Cosine normalisation N (1 + log( tf )) × log 1+ df √∑ w 2 i Pivoted normalisation 1+log( tf ) log( N × df ) 1+log( average tf ) (1 . 0 − slope ) × pivot + slope × # unique terms Information Retrieval (ISI) Introduction 19 / 20

  22. VSM: retrieval Measure vocabulary overlap between user query and documents. t 1 . . . t M Q = q 1 . . . q M D = d 1 . . . d M Q.⃗ ⃗ Sim ( Q, D ) = D = ∑ i q i × d i more matches between Q, D ⇒ Sim ( Q, D ) ↑ matches on important terms between Q, D ⇒ Sim ( Q, D ) ↑ Information Retrieval (ISI) Introduction 20 / 20

  23. VSM: retrieval Measure vocabulary overlap between user query and documents. t 1 . . . t M Q = q 1 . . . q M D = d 1 . . . d M Q.⃗ ⃗ Sim ( Q, D ) = D = ∑ i q i × d i more matches between Q, D ⇒ Sim ( Q, D ) ↑ matches on important terms between Q, D ⇒ Sim ( Q, D ) ↑ Use inverted list (index). t i → ( D i 1 , w i 1 ) , . . . , ( D i k , w i k ) Information Retrieval (ISI) Introduction 20 / 20

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