代写代考 BO1028/Teams (2 groups alternating every lab) on Fridays 14:00- 15:00 – Sch

IR H/M Course
Introduction to Information Retrieval
Information Retrieval 2022
A search task!

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Some Characteristics
Structured Query Structured Data Accuracy verified Useful Data is returned Exact match Answer meets query criteria

IR H/M Course
Information Retrieval
Information*Retrieval*is*the*science&of&search&engines
How*best*to*address*the*information*needs*of*users… • Effectively:”Get”the”right”information”to”a”user!
• Efficiently:”Get”it”to”users”quickly!
Information Needs
I”want”to”know”what” buildings”to”see”in” Glasgow
GGlalasgsgooww(W(beusitld(eingds(buildings

IR H/M Course
Search Engines
• The most visible application of information retrieval technologies are search engines
• Search engines have evolved since their initial conception 70 years ago
Search Engines
• Quite effective (at some things)
• Highly visible (some are very widely used)
• Commercially successful (some of them)
– Google is one of the biggest corporations in the world
• Underlying technology for searching ….. • What goes behind the scenes?
– How do they work?
• Let us have a look at the commercial Search Systems!

IR H/M Course
What features make Google & Bing different?

IR H/M Course
How do these systems work?
What are the commonalities & their functions?
Is there more to IR than Web Search?
In This Course, We Ask …
• What makes a system like Google or Bing tick?
– How does it gather information?
– What tricks does it use?
– Expanding beyond the Web?
• How can those approaches be made better?
– Natural language understanding?
– Machine learning?
– User interactions?
• What can we do to make things work quickly?
– Fast computers? Caching? – Compression?
• How do we decide whether it works well?
– For all queries? For special types of queries?
– On every collection of information?
• What else can we do with the same approach?
– Other media? Other tasks?

IR H/M Course
Definitions of Information Retrieval Salton, 1968
• Information retrieval is a field concerned with the structure, analysis, organization, storage, searching and retrieval of information
Needham, 1977
• The complexity arises from the impossibility of describing the content
of a document, or the intent of request, precisely, or unambiguously General definition
Retrieval of relevant information from data sources which were not originally intended for access (e.g. unstructured data )
What does this mean?
– Text (most often – e.g. searching newspaper articles or searching the
– Images… Video … Audio….
Documents vs. Database Records
• Database records (or tuples in relational databases) are typically made up of well-defined fields (or attributes)
• e.g. bank records with account numbers, balances, names, addresses, social security numbers, date of birth, etc.
• Easy to compare fields with well-defined semantics to a query in order to find matches
(Unstructured),text,is,more,difficult

IR H/M Course
Imprecision in IR
• Most algorithms in Computer Science have a “right” answer. In contrast, a heuristic tries to guess something close to the right answer.
• Considerthethreeproblems:
– Sort the following ten integers
– Find the highest integer
– Find the beers made by X (i.e. SELECT … FROM …WHERE …)
• Nowconsider:
• Find the documents most relevant to “hippos in the zoo”.
IR#techniques#are#essentially#heuristics#because#we#do# not#know#the#right#answer
What Makes a Document Relevant?
• It contains the query terms?
• It contains ALL of the query
• It contains the query terms many times? (…but what if it is a long document?)
• It contains the query terms many times in a short document?
• It contains the query terms close together?
• It is fresh/recent?
• It contains terms similar to the query terms?
• It is authoritative (has many links)?
• It doesn’t contain too many ads?
• It doesn’t contain too many different, unrelated words (e.g. spam)
• It has been clicked by many other people for the same query?
ALL of these are heuristics. They are not guaranteed to get a correct, relevant document for all users

IR H/M Course
Why to Retrieve?
• I need to find some information
– Who is the head of college of science & engineering? – How to get to the LUX city centre from LUX airport? – What is the upcoming topic in IR research?
– What to do this weekend in Glasgow?
• Where to search?
– Newspaper articles, Web pages, Scholarly materials (ACM/IEEE Digital Library), Emails, Tweets, …., Images (Flickr), …, Videos (YouTube)
– … and many more? Including a combination of all in your own desktop, … or in your enterprise…. or external sources like the Web!
Exact Need
Vague need
What do We Mean by Information?
• HowisitdifferentfromData?
– Information is data in context
– Databases contain data and produce information
– IR systems contain and provide information
• Howisitdifferentfrom
Knowledge?
– Knowledge is a basis for making decisions
– Many knowledge bases contain decision rules
Task type ?
Time available varies

IR H/M Course
What Do We Mean by Retrieval?
• Find something that you are looking for: – Ad hoc search
• Find documents “about this” topic-x
– Known item search
• Find the University of Glasgow home page
– Answer seeking
• WhatisthecapitalofBelgium?
– Directed exploration
• Who makes video conferencing systems?
– Decision making
• Best places to stay in Paris
– Expert search
• Who knows about Stable Marriage in my organisation?
Scenarios & Applications Web search was a “killer app”
• Developing advertising models on the web ….
Today there are many retrieval applications:
Microblogs
Classifieds
Search3over Sensors
Medical3Records
These types of content share common properties:
– Text content + some metadata (e.g. title, author, date for papers; subject,
sender, destination for email)
Role of Interaction

IR H/M Course
A Question-answer scenario
Question Answer
Assessment
Note 1: Asking a good question can be as hard as answering it
Note 2: The objective of the search engineer is to automate the above process
Search/IR Engineers are increasingly sought in industry
Retrieval Process
Remember: User need can be vague!
Query (approximate need?)
Retrieved Documents
Feedback Modified Query!
IR System capabilities
Given few query words
Infer what a user wants
Fetch relevant documents as fast as possible Present to users in a way they understand 20
Information Retrieval System
Even if the information need is Exact- there is a problem!

IR H/M Course
Query terms
An IR System
Retrieval System
Ranked Documents
Document Collection
Search logs
Multiple collections Real-life updates Streamed data Social media
Relevance … Effectiveness … Efficiency
• Relevance
– If the query and the document are about the same topic (known as the Topical Relevance) – Remember that a user’s query can be vague!!
• Effectiveness
– Looks into the quality of the retrieved set. Do they largely contain relevant documents? (role of Retrieval Models)
• Efficiency
– Return results as fast as possible (role of search engine architecture – indexes) – architecture of the IR
systems: distributing computation, updating indexes, etc.

IR H/M Course
IR H/M: General Information
• Structure
– Zoom lectures: Fridays 10:00-12:00 & [14:00-15:00]
– Labs: BO1028/Teams (2 groups alternating every lab) on Fridays 14:00- 15:00 – Schedule of lab sessions will be posted on Moodle
– Q/A forums on Teams
• Assessment
– Assessed coursework (20%) & Final exams (80%)
• Coursework:Ex1(4%),Ex2(8%),Ex3(8%) • Lecturer (s):
Yours truly Craig
… & various tutors/guests
@craig_macdonald

Objectives
• At the end of this course you will be able to…
– Explain the process of retrieval
– Build an IR system and deploy it for practical
applications
You may need a retrieval component in your own project work
– Explain how IR systems are evaluated
– Understand the web search engine architecture
– Explain advanced IR technologies & Applications
• Learning to rank; diversification; personalization, …. • Empower you with a necessary set of skills
– Develop and deploy IR systems or related technologies 24

IR H/M Course
Planned Syllabus
Teaching Resources
• Text Books – Recommended ( available online!!)
– Search Engines (Information Retrieval in Practice), , Metzer, Strohman, 2015
– Introduction to Information Retrieval
, Manning, Raghavan, Schutze (eds), Cambridge University Press 2008
• Course Web page is available on Moodle
– Updated regularly as we go
– News/material regarding the course will be posted on Moodle;
Interactive quizzes
• Relevant (online) Seminars
– Mondays, 3-4pm; All are welcome
– Announced on the school web pages

IR H/M Course
First 3 Weeks: Look at the Basics of IR
• Architecture of the System
• Concepts of Relevance & Ranking • Text Normalisation techniques
• IR Evaluation

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