Comp-8380: Information Retrieval

what is IRcourse schedulegrading scheme

Comp-8380: Information Retrieval

Jianguo Lu

January 10, 2021

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Outline

1 what is IR

2 course schedule

3 grading scheme

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Outline

1 what is IR

2 course schedule

3 grading scheme

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IR not long time ago

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now IR is mostly about search engines

there are many search engines ...

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IR is more than web searchThese days we frequently think first of web search, but there aremany other cases:

digital library searchE-mail search, Searching your desktop and laptop computersCorporate knowledge bases, local business search, expertsearchLegal information retrieval, patent searchnews searchimage and video search(micro-)blog searchproduct search, federated searchsocial search, community Q&A, question-answeringrecommender systemsopinion mining

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definition of information retrieval

Information retrieval (IR) is finding material (usually documents) ofan unstructured nature (usually text) that satisfies an informationneed from within large collections (usually stored on computers).

–from IIR book.Introduction to Information Retrieval, by C. Manning, P.Raghavan, and H. Schutze. Cambridge University Pressbook website https://nlp.stanford.edu/IR-book/

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Structured vs. unstructured data

in the 90’s. todayInformation retrieval is finding material of an unstructured naturethat satisfies an information need from within large collections

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other definitions

Jaime ArguelloInformation retrieval (IR) is the science and practice ofdesigning, developing, and evaluating systems that matchinformation seekers with the information they seek.

Gerard Salton, 1968:Information retrieval is a field concerned with the structure,analysis, organization, storage, and retrieval ofinformation.

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The search task

Given a query and a corpus, find relevant itemsquery: user’s expression of their information needcorpus: a repository of retrievable itemsrelevance: satisfaction of the user’s information need

Corpus: definition from Webstera : all the writings or works of a particular kind or on aparticular subject; especially : the complete works of an authorb : a collection or body of knowledge or evidence; especially :a collection of recorded utterances used as a basis for thedescriptive analysis of a language

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Why is IR fascinating?

Information retrieval is an uncertain processQuery

users don’t know what they wantusers don’t know how to convey what they wantcomputers can’t elicit information like a librariancomputers can’t understand natural language text

Relevancethe search engine can only guess what is relevantthe search engine can only guess if a user is satisfiedover time, we can only guess how users adjust their short- andlong-term behavior

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classic search model

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A query is an impoverished description of the user’sinformation needHighly ambiguous to anyone other than the user

Retrieval ModelA formal method that predicts the degree of relevance of adocument to a query

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taxonomy of IR models

Document Propertytextlinks

multimedia

IR modelsBooleanvector

probalistic

Semistructured textproximal nodes

xml based

webpage rank

hubs and authorities (HITs)

Multimediaimage retrieval

audiovideo

Set theoreticfuzzy

extended booleanset-based

algebraicgeneralized vector

LSINN

probablisticBM25

language modelsBayersian networks

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Boolean Retrieval Model

The user describes their information need using booleanconstraints (e.g., AND, OR, and AND NOT)The burden is on the user to formulate a good boolean query

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Example

Which plays of Shakespeare contain the wordsBrutus AND Caesar but NOT CalpurniaOne choice: use grep command in unix.

grep all of Shakespeare’s plays for Brutus and Caesar,strip out lines containing Calpurnia

Why is that not the answer?Slow (for large corpora)NOT Calpurnia is non-trivialOther operations (e.g., find the word Romans nearcountrymen) not feasibleRanked retrieval (best documents to return)

so we need to index the text

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Example





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Example





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what is an index

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index construction process

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Initial stages of text processing

TokenizationCut character sequence into word tokens

NormalizationMap text and query term to same form

You want U.S.A. and USA to matchStemming

We may wish different forms of a root to matchauthorize, authorization

Stop wordsWe may want to omit very common words (modern methodsmay not)

the, a, to, of

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postings

Multiple term entriesin a single documentare merged.Split into Dictionaryand PostingsDoc. frequencyinformation is added.

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query processing

Consider processing the query:Brutus AND Caesar

Locate Brutus in the Dictionary;Retrieve its postings.Locate Caesar in the Dictionary;Retrieve its postings.ÒMergeÓ the two postings (intersect the document sets):

brutus 1 2 4 11 31 45 173 174

caesar 1 2 4 5 6 16 57 132

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Outline

1 what is IR

2 course schedule

3 grading scheme

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tentative schedule

boolean modeltext transformationbuild a search engine using Lucenevector space modelrepresentation learningevaluation methods in information retrievallink analysis and PageRankdocument classificationdocument clusteringweb crawling. Data cleaning (e.g. near-duplicate detection)

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Text Book

[IIR] Introduction to Information Retrieval, by C. Manning, P.Raghavan, and H. Schutze. Cambridge University Press, 2008.

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Other reference books

SE Search Engines: Information Retrieval in Practice, by BruceCroft, Donald Metzler and Trevor Strohman.

MIR Modern Information Retrieval, by R. Baeza-Yates and B.Ribeiro-Neto. 2-nd edition 2010.

MMD Anand Rajaraman and Jeff Ullman, Mining of massivedatasets , 2013.

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IIR 02: The term vocabulary and postings lists

Phrase queries: “Stanford University”Proximity queries: Gates near MicrosoftWe need an index that captures position information forphrase queries and proximity queries.

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IIR 04: Index construction

masterassign

mapphase

reducephase

assign

parser

splits

parser

parser

inverter

postings

inverter

inverter

a-f

g-p

q-z

a-f g-p q-z

a-f g-p q-z

a-f

segmentfiles

g-p q-z

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statistic properties of text

0 1 2 3 4 5 6 7

01

23

45

67

log10 rank

log

10

cf

Zipf’s law, heaps’ law, power law.the mechanism: Yule process, Preferential attachment

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IIR 06: Scoring, term weighting and the vector space model

Ranking search resultsBoolean queries only give inclusion or exclusion of documents.For ranked retrieval, we measure the proximity between the query andeach document.One formalism for doing this: the vector space model

Key challenge in ranked retrieval: evidence accumulation for a term ina document

1 vs. 0 occurence of a query term in the document3 vs. 2 occurences of a query term in the documentUsually: more is betterBut by how much?Need a scoring function that translates frequency into score or weight

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Language models

assign a probability to a sequence of m words by means of aprobability distribution.How to compute this joint probability:

P(its,water, is, so, transparent, that) (1)P(w1w2 . . .wn) = ΠP(wi)? (2)

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Text classification & Naive Bayes

Text classification = assigning documents automatically topredefined classesExamples:

CS vs. Non-CS papersPapers in Software Engineering vs. Databasepositive/negative reviewsSpams

Naive Bayes (Multinomial and Bernoulli model), Supportvector machine, feature selection, representation learning,neural networks.

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Neural network based representation learning

Answer analogical questions, e.g

Man : Woman = King :?

The answer will be Queen.An application of deep learning

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clustering

Flat clusteringHierarchical agglomerative clustering (HAC)Single-link and complete-link clusteringCentroid and group-average agglomerative clustering (GAAC)Bisecting K-meansHow to label clusters automatically

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HAC

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Latent Semantic Indexing

how to find semantically related documents?matrix decompositionSVD

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Crawling

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Link analysis / PageRank

which web page is more important?who are in a community?PageRank algorithmgraph analysis and mining. Modularity maximizationalgorithms.

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Outline

1 what is IR

2 course schedule

3 grading scheme

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marking scheme

exam 50%project 50%

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project

text analysisgraph analysisbuild searching engineenhance the search engine by adding one or more features,such as:

semantic searchclassificationclustering (returning results (papers) are clustered into severalareas)ranking (ranked by PageRank algorithm)personalizationrecommendation (recommend most similar papers)...

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The projectThe tentative plan for the project is:

10%: Phase one. Implement one of the project topicsdiscussed in class

Workable in Jupyter Notebook.Have good explanation in Notebook MarkdownPresentations finish before reading weekEarlier presenters choose the topic they want.Later presenters need to implement and present differentfeatures.Example topics: text statistics, smoothing, Naive Bayesclassification, Word embedding, Graph embedding.

25% Phase two. Add one more topic and improve your firsttopic.

Rank documents using the PageRank algorithm using citationdataReturn results by categories (By running clustering algorithms)Search for similar papers (e.g., running doc2vec)Finish by

15% Phase three: Implement the search engine in Lucene, andpossibly integrate the results from phase one and two. (e.g.,for for similar documents, suggest search queries).

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open source search engines

LuceneJava-basedrelatively simple IR techniques

GalagoJava-basedused by the book [SE] Search Engines: Information Retrievalin Practice, by Bruce Croft et al.

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Documents

Comp-8380: Information Retrieval