代写代考 BM25 (怎么决定文件document是relevant还是irrelevant; python代码后出来的relevant还是irrelevant

Report guide 1:

All steps => All five steps的思路步骤都要写，5步在这个pdf里

Copyright By PowCoder代写 加微信 powcoder

Report guide 2:
Introduction
• describe three models
(需要把课程pdf提到的三个算法相关的mechanism都写清楚，最相关的部分是wk8~wk11):
（下面是代码中三个模型必写的相关mechanism）
BM25 (怎么决定文件document是relevant还是irrelevant; python代码后出来的relevant还是irrelevant; filtering system机制详解)
Model1 (prm的query从哪来; prm的伪标签怎么来的)
Model2 (逻辑回归的training set是什么从哪来)
• Assumptions
• Algorithms
• Describe your development (which packages)
Results & Evaluation
• Use of multiple effectiveness measures
Discussion
• Analysis about your findings Recommendation
• Results + statistical measures –>t-test (T-test解释)
• must include a justification on your result
Limitations
Conclusion
Statement of Completeness
User Manual
• Explain how to execute the system

describe three models:
bm25 week10 IRM
model1 week 10 PRM,
model2 同样是prm，但是使用池袋模型表示文档，然后使用logestic回归分类
Algorithms:
Bm25 w5 logestic回归
Describe your development (which packages):
Bm25 前两部分主要用了math这个库， model2使用sklearn机器学习库，pandas来读取数据
solution for all steps
前俩个参考你们作业就行了，里面步骤很详细
Model2 对于某个query中的全部文件，使用CountVectorizer（）编码，得到特征，然后使用于model1中类似的方法，得到文件的伪标签，用这些数据训练逻辑回归模型，最后使用模型预测的可能性作为排名的依据。

IFN647_asm2-20

Microsoft Word – IFN647_asm2-2022.doc

IFN647 – Assignment 2 Requirements
Weighting: 35% of the assessment for IFN647

Deliverable Items

1. A final report in PDF or word file, which includes
• Statement of completeness and your name(s) and student ID(s) in a cover

• Your solution for all steps (See more details in the “Requirements for each

step” session).
• User manual section describing the structure of the data folder setup,

information on “importing packages” and how to execute Python code in
Terminal, IDLE or PyCharm; and

• An appendix listing the top 10 documents for all topics and all models.

2. Your source code for all steps, containing all .py files (using a zip file “code.zip” to
put them together) necessary to run the solution and perform the evaluation (source
code only, no executables); and

3. A poster and demo to your tutor during your week 13 workshop time.

Please zip all files (the final report, code, and poster) into a zip file as your
“studentID_Asm2.zip” and submit it through the Blackboard (a single submission only
for a group) before the due date.

Please do not include the dataset folder generated by “DataCollection.zip” or
“RelevanceFeedback.zip” in your submission.

The following are the frameworks/libraries that you can used for assignment 2:

(a) sk-learn
(c) pandas
(e) Matplotlib

If you want to use another package or library, you need to get your tutor’s approval.

Due date of Blackboard Submission: Sunday week 13 (12th June 2022)

Group: You are required to work on this assignment in a group of three people.

Due to the difficulty of automatically obtaining user information requirements, most search
systems only use queries (or topics) instead of user information requirements. The first reason is
that users may not know how to represent the topics they are interested in. The second reason is
that users may not want to spend a lot of effort mining relevant documents from the hundreds of
thousands of candidates provided by the system.

This unit discusses several ways to extend query-based methods, such as pseudo-relevant
feedback, query expansion or hybrid methods. In this assignment, for a given data collection
(including 50 datasets), you need to discover a good information filtering model that
recommends relevant documents to users on all 50 topics, where documents in each dataset are
collected for a corresponding topic. The methodology you will use for this assignment includes
the following steps:

• Step 1 – Design an IR-based baseline model (BM_IR) that ranks documents in each
dataset using the corresponding queries for all 50 datasets.

• Step 2 – Based on the knowledge you gained from this unit, design two different models
to rank documents in each dataset using corresponding queries from all 50 datasets. You
can call them Model_1 and Model_2, respectively.

• Step 3 – Use python to implement three models: BM_IR, Model_1 and Model_2, and
test them on the given data collection of 50 topics (50 datasets) and print out the top 10
documents for each dataset (put the output in the appendix of your final report).

• Step 4 – Choose three effectiveness measures to display testing results against the
selected effectiveness measures in tables or graphs.

• Step 5 – Recommend the best model based on significance test and your analysis.

Data Collection

It is a subset of RCV1 data collection. It is only for IFN647 students who will be supervised by
Prof. Li Yuefeng. Please do not release this data collection to others.

DataCollection.zip file – It includes 50 Datasets (folders “dataset101” to “dataset150”)
for topic R101 to topic R150.

“Topics.txt” file – It contains definitions for 50 topics (numbered from R101 to R150)
for 50 datasets in the data collection, where each element (…) defines
a topic, including topic number (), title (