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FIT5212 - Assignment
创建日期：2024-08-17 15:12:38
标签： FIT5212
Assignment

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FIT5212 - Assignment 1


Marks

Worth 50 marks, and 25% of all marks for the unit

Due Date

Due Week 7 – Lecture Date at 23:55pm

Extension

An extension could be granted under some circumstances. A special consideration application form. must be submitted. Please refer to the university webpage on special consideration.

Lateness

For all assessment items handed in after the official due date, and without an agreed extension, a 10% penalty applies to the student’s mark for each day after the due date (including weekends) for up to 10 days. Assessment items  handed in after 10 days without special consideration will not be considered.

Authorship

This is an individual assessment.  All work must be your own. All submissions will be placed through Turnitin. This makes plagiarism remarkably easy to identify for us.

Submission

Submission is 3 files:  one PDF discussion report, and one Jupyter notebook with a PDF print of it.  The three files must be submitted via Moodle. All files will go through Turnitin for plagiarism detection.

Programming

language

Python in Jupyter

Part 1:  Text Classification

The content has been gathered from the popular academic website arXiv.org for articles tagged as computer science content (though some of these are in mathematics or physics categories). This spans 2024-2016. You are given 3 csv files: train/dev/test sets. The fields in the csv files are:

. Title: the full title

. Abstract: the full abstract

. InformationTheory: a "1" if it is classified as an Information Theory article, otherwise "0".

. ComputerVision: a "1" if it is classified as a Computer Vision article, otherwise "0".

. ComputationalLinguistics: a "1" if it is classified as a Computational Linguistics article, otherwise "0".

The three classes are ComputationalLinguistics, InformationTheory and ComputerVision. These can occur in any combination, so an article could be all three at once, two, one or none. Your job is to build a text classifier that predicts the class ComputationalLinguistics using the Abstract field. Then repeat the same experiment using only the Titles. You should train different text classifiers using different configurations for this binary prediction task. The variations we would like to consider are:

1.   Task: 1 binary classification task (ComputationalLinguistics vs. Other two classes)

2.   Input: use Abstract, and Titles alone (separate configurations)

3.   Algorithm:  use 2 different algorithms from tutorials, use the RNN and then choose one of the statistical classifiers (logistic regressions, SVM, etc)

4.   Data size: train on the first 1000 cases in the training set, and then train on the full the training set.

5.   Pre-processing: Choose a data pre-processing procedure (i.e., lemmatization, stemming, removing stop words, etc) and stick with it in all your experiments.

So this makes 2 (i.e., abstract vs. title) by 2 (i.e., 2 algorithms) by 2 (i.e., 2 training sizes) different configurations.

For each configuration test the algorithm on the test set (Note: when testing for the model trained on the Abstracts, you should use only the Abstracts oftest set. Similarly for testing for the model trained on the Titles, you should use only the Titles oftest set.) provided and report the following results in your notebook

.    F1, precision, recall, accuracy

.    precision-recall curve

being creative about how you assemble the different values and plot the curves. The discussion of these results should be in its own 2 page discussion section in the PDF report. How well did the two algorithms work under different data size conditions, when and why?  How the model trained on title compared with the one trained on the abstracts? What insights do the various metrics and   plots give you?

Part 2:  Topic Modelling

The data used is the training data from Part 1.  Your job is to perform appropriate text pre-processing and preparation and then design two different variations for running LDA using the gensim.models.LdaModel()function call and pre-processing steps such as given in the tutorial. Select appropriate choice of pre-processing and parameters to develop model outputs that are informative. Choices you might make in differentiating the two variations are:

.    different pre-processing of text or vocabulary

.    use of bi-grams or not

.    different numbers of topics (e.g., K=10, K=40)

Now run these two on the first 1000 and the first 20,000 articles in the training data set. This means there are 2 by 2 different configurations for the LDA runs. Then make visualisations of some kind in the notebook. These should allow you to analyse and interpret the output of the topic models.

The actual discussion (analysis and interpretation) about the results should not appear in the notebook but be in the separate PDF discussion report. This is a 2 page discussion giving your analysis and findings that were presented in the notebook output. What sorts of topics do you see?

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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