SIT772 database and information Assignment 2

Assignment 2

Introduction

• This assessment is for students to develop the understanding of information retrieval techniques.
• This is an individual assessment task.
• The project documentation submitted should include the answers, working, associated tables and graphs to the tasks
• This assignment has a total of 20 marks and is worth 20% of your final result

Unit Learning Outcomes

• Of the three Unit Learning Outcomes (ULOs) of this unit SIT772, this assessment task will focus on the last two ULOs. These are: o ULO 3 - At the end of this unit students will be able to design and develop relational databases by using SQL and a database management system. o ULO 5 - Develop problem solving skills in the context of data processing systems.

o ULO 6 - Work independently on self-directed learning tasks

• The assessment of this task will indicate whether students can partially attain these unit learning outcomes.

Instructions

• Read these instructions and the following 2 questions.
• Answer as many questions as possible.
• Place your name, ID and answers in your document.
• Please answer all questions in a single document and submit this to the assignment folder.

Task 1: Zipf’s Law (5+5=10 Marks)

1. Provide a brief description of Zipf’s Law and how this is related to information retrieval (searching for term/words in a corpus).
2. Assuming Zipfs law with the most frequent term appeared 20% of word occurrences. What is the fewest number of most common words that together account for more than 60% of word occurrences (i.e. the minimum value of m such that at least 60% of word occurrences are one of the m most common words). You can use a table to help present your result.

Task 2: Information Retrieval (IR) Evaluation (3+3+4=10 Marks)

The following data displays retrieval results for two different algorithms (Algorithm 1 and Algorithm 2) in response to two distinct queries (Query 1 and Query 2). An expert has manually labelled each of the documents as being either relevant or not relevant to the queries.

Algorithm 1 Returns the following results:

An expert has identified the following documents as being relevant to Query 1 and Query 2, respectively.

Objectives:

1. For Algorithm 1, plot the precision versus recall curves for Query 1 and Query 2, interpolated to the 11 standard recall levels. Also plot the average precision versus recall curve for Algorithm 1 (all three curves should be on a single chart).
2. For Algorithm 2, plot the precision versus recall curves for Query 1 and Query 2, interpolated to the 11 standard recall levels. Also plot the average precision versus recall curve for Algorithm 2 (all three curves should be on a single chart, but a separate chart from that used in part (a)).
3. Plot the averages for Algorithm 1 and Algorithm 2 on a separate chart, and compare the algorithms in terms of precision and recall. Do you think one of the algorithms is superior? Provide a brief explanation of why this is the case?