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SIT772 Database and Information Retrieval
创建日期：2024-04-26 10:32:44
标签： SIT772
Database and Information Retrieval

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Unit Information: SIT772 Database and Information Retrieval


Assessment 2: Information Retrieval Problem Solving Task

This document supplies the detailed information on assessment tasks for this unit.

Key information
• Weighting: 30%
• Submit: Through CloudDeakin

Learning Outcomes
This assessment assesses the following Unit Learning Outcomes (ULO) and related Graduate
Learning Outcomes (GLO):

Unit Learning Outcome (ULO) Graduate Learning Outcome (GLO)
ULO 5: Demonstrate data retrieval skills in
the context of a data processing system.
GLO 1: Discipline-specific knowledge and
capabilities

Purpose
This task evaluates the student's technical skills in the management of unstructured data, with
potential usage in real applications. This assessment supports student understandings of the
techniques related to unstructured data management and data processing
Instructions and Submission Guide
This is an individual assessment task. Students are required to submit ONE written report.
• Read these instructions and the following questions.
• ONE written report with the name as using student ID_givenname_A2.pdf, e.g.,
123456_Kevin_A2.pdf )
• The report must be submitted via CloudDeakin assessment portal. The wrong
submission venue or the wrong submitted file may lead to the penalty.

Question 1: (6- 4+2)
Try and find a Query of the form [Query-term-1, Query-term-2] (without quotes) that, when run
on Google, produces at least one result that contains only one of TWO terms. That is, try to find
an example where Google does not interpret a the-term query as a conjunction. (If you have
difficulty with finding an appropriate query, try one that produces very few hits, say, fewer than
20.)
(i) Take screenshot of the first page of Google results (or more if you want to) and mark
each result with 2 (both terms occur on the page), 1 (one term occurs on the page) or
0 (neither term occurs on the page)
(ii) Based on this evidence, does Google interpret all queries as a Boolean conjunction?
Explain.

Question 2: (16: 8+4+4)
Recall and Precision are two important evaluation metrics that we use to analyze a set of
unranked results. Precision and Recall metrics consider the differences between set of
documents retrieved for given query and the set of documents that are relevant to the user’s
need.
A) Compute Recall, Precision and precision@5 for the following retrieval against Queries
Q1, Q2 and Q3
Relevant document Retrieved Document
Q1 1,14,17,23, 24, 33,54, 55, 59,
74,101,103
2,5,7,23, 33,50, 55, 59, 77,98, 99,
101, 103, 110,120
Q2 14,19, 25, 27,30,39, 42, 63, 769,
790,1563
14, 21, 25, 26,27, 38, 42, 63, 569,
769, 790, 1565, 1589
Q3 8, 11,32,54,67,69,78, 79,
91,99,111,122
11, 13, 17, 19, 21, 32,77,79,
99,102,111,122
Q4 4,26, 38, 63, 569, 769, 790, 1565, 1589 14, 21, 25, 26,27, 38,63, 88, 769,
790,

B) Recall and Precision are often discussed together as their focus is on complementary
information. If precision is important, the we don’t not want to see any non-relevant
documents. That is, whatever is retrieved, should be relevant. If recall is important, we
want to see all the relevant documents, even if it requires sifting through some non-
relevant ones. Provide and Justify two information-seeking tasks where precision may be
considerably more important than recall. Similarly, Provide and Justify two information-
seeking tasks where recall may be more important than precision. [Don’t forget to justify
your choices: Justification will be graded, not the particular choices].

C) The trade-off between Recall and Precision may be user-specific i.e. some users may be
interested in precision than recall and vice versa. How the search engine try to guess
without asking, whether user cares more about precision than recall, or vice versa?
Think of different ways, users interact with a search engine and be creative!




3

Question 3: (6: 3x3)
(a) Consider, we have three collections C1, C2, and C3 that have 500, 15,000 and 300,000
documents respectively. We have added C1 or C2 into C3. Which collection is likely
to have more new terms added to its vocabulary (C1+C3 or C2+ C3) and why?
(b) Calculate the tf-idf for below documents.
a. D1: Sweets Potatoes are Sweet
b. D2: Sweet Oranges are sour and Sweet
c. D3: I have sweet Apple, Sweet Orange, Sweet Potatoes

Question 4: (10-5x2)

Doc-id house for sale in Geelong Melbourne
1 39 11 32 22 22 4
2 19 19 3 15 16 21
3 19 20 1 3 21 9
4 12 20 14 1 13 13

➔ (houses OR for OR sale OR in OR Geelong OR Melbourne)
➔ (houses AND for AND sale AND in AND Geelong OR Melbourne)
Suppose these are issued to a search engine that uses the ranked Boolean retrieval model.
Assume, for simplicity, only four documents in the collection (with document ids 1-4).
Answer the following questions. The above table gives the number of times each query-term
occurs in each document.
(i) Compute the document scores and the ranking associated with the query (houses OR
for OR sale OR in OR Geelong OR Melbourne).
(ii) How is the ranking produced probably sub-optimal and why does this happen?
(iii) Compute the document scores and the ranking associated with the query (houses AND
for AND sale AND in AND Geelong OR Melbourne).
(iv) How is the ranking produced probably sub-optimal and why does this happen?
(v) How would you extend the Boolean retrieval model to handle AND NOT constraints
(e.g., houses AND NOT Geelong)? Your proposed solution should give a higher score
to documents that contain fewer occurrences of the term to the right of the AND NOT
(e.g., Geelong). Please be as mathematical as possible. In other words, saying: “I would
reduce the score for documents that contain the word to the right of AND NOT.” is too
vague.
(vi) Using the index, what would be the Boolean retrieval model scores given to documents
1-4 by your proposed scoring method for the query “houses AND NOT Geelong”?

Question 5: (12-4x3)

Doc1: A book is considered a good book that makes the reader feels better.
Doc2: I love reading good books to feel better.
Doc3: One can feel better after reading Tom’s recent book.

4

Query-1: I love books that are good
Query -2: reading good books make you feel better
Stop Word Dictionary=[is, can, after, a, to, I, the, about, that]

i. Explain the similarity scores of both Query -1 and Query -2 using TF-IDF.
ii. How would the result change if TF-IDF is used instead of TF as Query?
iii. What do prefer using TF or TF-IDF as Query (Support your claim using F-score).

Assessment feedback
General feedback to the class will be provided via CloudDeakin-Discussion Forum. The formal
assessment feedback will be released with the marks in CloudDeakin altogether.

Extension requests
Requests for extensions should be made to Unit/Campus Chairs 3 days early before the
assessment due date.

Special consideration
You may be eligible for special consideration if circumstances beyond your control prevent you
from undertaking or completing an assessment task at the scheduled time.

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MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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