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INF6060: Information Retrieval
Creation date：2024-04-24 17:23:00
Belonging category：计算机computer science
Tag INF6060
Information Retrieval

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INF6060: Information Retrieval

Coursework overview
• Assignment 1: Tasks and queries (10%).
• Assignment 2: Portfolio of evaluations with
recommendations (90%).
IMPORTANT you MUST also read the Coursework Brief
and FAQ.
Assignment 2: Portfolio of evaluations with
recommendations (90%).
Three sections to assignment 2
1. Tasks and queries
2. Activities 1 - 4
3. Recommendations
Gratuitous photo of a cat!
Assignment 2: Tasks and queries
Section 1 What we are looking for Marks Supported in
Tasks and
queries
All tasks (simulated work tasks and search tasks) and
queries used in the evaluations (guideline up to 100 words
per simulated work task).
1 mark
deducted from
presentation if
not included.
-
Reflection A brief explanation of any changes made to the
assessment 1 tasks and queries (guideline 100 words).
1 mark
deducted from
presentation if
not included.
-
What we are looking for - tasks and queries
• Are the tasks and queries realistic? Can they be used to conduct a fair
test of the system?
• Have you taken on board assignment 1 feedback?
• Have you revised your tasks and queries as you become more
knowledgeable about IR and the Sheffield University Website Search
System? HINT!
Assignment 2: Activities 1 - 4
Section 2 What we are looking for (minimum requirements) Marks Supported
in
Activity 1: heuristic
evaluation
A table that lists the 10 Nielsen heuristics and provides a brief description of whether the search
interface deals with the heuristic effectively for 1 SWT. A brief summary of the findings.
15 Week 7
Activity 2: text tokenisation
and processing strategy
A logical strategy for applying tokenisation to text documents to be indexed by the search system. You
should justify any assumptions you make when generating this strategy.
15 Weeks 4 & 5
Activity 3: measuring
retrieval system
effectiveness
You should submit 3 different queries for each of your SWTs (i.e. 6 in total) to the University search
system and assess the objective performance of the system based on your chosen queries. This will
require you to create relevance judgements for the results of each of your queries and to assess the
results using appropriate performance metrics up to rank 10. You should state any assumptions you
made when assessing retrieved documents for relevance.
15 Week 6
Activity 4: supporting the
users’ search process
A table that identifies features of the search system. Features should be categorised using Wilson
(2011) and the purpose of each feature should be succinctly described. Brief description of how the
user may carry out the two search tasks utilising the categorised features. You should structure this
description around a search process model such as Sutcliffe & Ennis (1998). Short conclusion on how
well the system supports the two task types.
15 Week 8
Activity 1: Heuristic Evaluation
Coursework brief
• A table that lists the 10 Nielsen heuristics and provides a brief
description of whether the search interface deals with the heuristic
effectively for 1 SWT.
• A brief summary of the findings.
Lecture week 7
• We practiced conducting an heuristic evaluation in class using the
Birmingham website search system (remember to use Sheffield for
your coursework)
Conducting your heuristic evaluation (I)
• You will be the evaluator
• Evaluate the search interface against Nielsen’s heuristics
• Use your SWT when conducting the evaluation
• Evaluate the search interface iteratively
• Pass 1: get a feel for the system and interaction
• Pass 2+: focus on specific elements (and how they fit with the overall
interaction)
Conducting an heuristic evaluation (II)
We ask you for: A table that lists the 10 Nielsen heuristics and provides
a brief description of whether the search interface deals with the
heuristic effectively for one SWT
• Identify positive and negative aspects of the design
• You can use screenshots to illustrate your findings (if you think it
helps)
Heuristic Positives Negatives
1. Visibility of system status System indicates if additional /
replacement terms were searched for
(figure 1)
…
More examples … …
2. Match between system and real world … ….
Conducting an heuristic evaluation (III)
• We ask you for “a brief summary of the findings”.
• Think about the severity of the problem. You could refer to one of
these schemes
• Frequency, impact and persistence of positives and negatives.
www.nngroup.com/articles/how-to-rate-the-severity-of-usability-problems
• Showstopper, Major issue, Irritant (Kirmani, 2008)
What we are looking for – heuristic evaluation
• How the search system performs for each heuristic (e.g. strengths
and weaknesses).
• Correct application of each heuristic
• Clear and well written summary. Could also include screenshots.
You may also wish to extend this evaluation so that you can make more
evidenced based recommendations in section 3
• More than one SWT, device or comparison with other university
website
Activity 2: text tokenisation and processing
strategy (I)
Coursework brief
• A logical strategy for applying tokenisation to text documents to be indexed by the search system.
Lecture 5 RECAP - you practiced this already!
1. Enter a query into the Sheffield search engine based on one of your SWTs
2. Choose a single relevant document and copy and paste its first paragraph/first few sentences
3. Define tokenisation rules and apply them
4. Choose set of 5 stopwords and apply them
5. Create 2 stemming rules and apply them
What we are looking for
• Accurate application of rules
• Description of logical tokenisation rules (including term separation characters; treatment of case and
punctuation) with justification.
• Description/summary of additional processing steps (e.g., stopword removal)
• Concrete examples of how the strategy would transform a raw text document.
Activity 2: text tokenisation and processing
strategy (II)
Coursework brief:
• You should justify any assumptions you make when generating this
strategy
What we are looking for:
• A discussion of assumptions and their positive/negative implications.
Activity 3: measuring retrieval system
effectiveness
You should submit 3 different queries for each of your SWTs (i.e. 6 in total) to the University search
system and assess the objective performance of the system based on your chosen queries. This will
require you to create relevance judgements for the results of each of your queries and to assess the
results using appropriate performance metrics up to rank 10. You should state any assumptions you
made when assessing retrieved documents for relevance.
What we are looking for
• Correct use of metric(s) according to search task (e.g. query for known-item should use Reciprocal
Rank rather than P@10). Recall is not really appropriate here.
• Clear and reproducible methodology including engagement with the appropriate literature.
• Clear presentation of results in tabular form.
• Some commentary on how relevance judgements were made. Typically binary, but could be
multi-graded (highly relevant, partially relevant and not relevant). If multi-graded does not work
with P@10.
• Some analysis of different query types.
Activity 4: supporting the users’ search
process (I)
Coursework brief:
• A table that identifies features of the search system. Features should
be categorised using Wilson (2011) and the purpose of each feature
should be succinctly described.
Lecture week 8
• You made a start on this
Activity 4: supporting the users’ search
process (II)
Coursework brief:
• Brief description of how the user may carry out the two search tasks
utilising the categorised features. You should structure this description
around a search process model such as Sutcliffe & Ennis (1998).
• Short conclusion on how well the system supports the two task types.
Questions to think about:
• Did you use different search features for the two search tasks?
• Did the system perform better for different tasks?
• Were there features that you thought were missing?
• Were some stages of the search process better supported than others?
What we are looking for
• Table showing a good number of features based on Wilson’s
categories.
Case category
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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