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Information Retrieval Lecture Summary

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Summary for the course Information Retrieval given in the third year of Artificial Intelligence, for the Data Science minor. This document efficiently summarizes the lectures and is a great way to study for your exams. This can also be relevant material for similar degrees given in Vrije University...

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  • February 16, 2022
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  • 2021/2022
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Lecture 1




History of Information Retrieval

Automated IR

' '
first idea in 1945 As
'

:
we think
may
-




microfilm mechanical recordings with links between books


In 60 's field emerged



the ,
the IR




Evolution of Information Retrieval

1960-1970 's the era Boolean Retrieval
-

:
of




1975 :
first vector space Model

1980 's available
large DBS made
-
:




1990 's FTP search and dawn web
-




of
:




IR in the 2000 'S

link
analysis & retrieval
Coogle)
-




Q&A
'




Multimedia IR C
image / video)
'




cross
-




languages
-




-




semantic web CDBPedia)


IR since 2010 's


categorization and
clustering and recommendations :


" "


Top songs
-




" "

People who bought this also
bought
i




IBM Watson
-




video / media recommendations ( youtube netflix)
-




,




!
knowledge graphs

IR vs DB

IR :
DBS :




cmostly) unstructured data structured
- .




Key words ( loose semantics) defined query language

-




incomplete query specification complete specification
'




query
-



, ,




partial matching exact matching
relevant items for result single failure
-




error
-
=
,




errors aoe tolerable
-


deterministic models

probabilistic models
-

, The information retrieval framework




search speed




criteria for Good Search Engines
How fast does it search ?
-





Latency as a function of index size

How fast does index ?
-



it


↳ Number documents / hour
of
-



Expressiveness of query language

Ability to express complex information needs

↳ speed on complex queries
Disk requirements





space



User bossiness

,Lecture 2 :
Indexing and Boolean Retrieval



Term -
Document Incidence Matrix


r




Incidence vectors

vectors with 011 for each term




Bigger collections

consider a text collection of N =
1 million documents ,
each with about 1000

words (tokens) :




On -6
bytes / word C including
-




and
average ,
we
might have spaces

punctuation)
-


.
: 6GB of data in the documents

would consist about IVI 500 000 distinct
:
vocabulary
-
=
-

our of
words (terms)



Matrix is Getting very Big
500K ✗ IM = 1 trillion

matrix Cie zeros)
belt ,
the is
very sparse .


mostly .




↳ is 1 's
per column ,
there a maximum
of 1000 compared to 500K words

i.
better representation :
only record Is !




Inverted index

-


For each chord ) t ,
we store a list
of all documents that contain t


Identify each
by document ID serial document number
-


a :




need C- lists)
we variable size lists
posting


-




continuous best
disk run
postings is
-




on , of
linked lists variable
-




in memory
we can use or
length arrays
-


,

, Inverted Index Construction




Indexer seep 1 : Token sequence
Exact sequence of < normalized token , document ID >
pairs




Indexer seep 2 : sort




Indexer sleep 3 :
Dictionary and Postings
Multiple term entries in a
single document are merged :




split into dictionary and postings





document
frequency information is added
-

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