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CS584: Natural Language Processing
创建日期：2024-04-16 15:21:32
标签： CS584
Natural Language Processing

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CS584: Natural Language Processing 

Assignment 1: Logistic Regression Homework assignments will be done individually: each student must hand in their own answers. Use of partial or entire solutions obtained from others or online is strictly prohibited. Electronic submission on Canvas is mandatory. 1. Document Classification (100 points) In this homework, you need to classify news articles into four categories: World, Sports, Business, and Science/Technology by building your own Logistic Regression classifier. The data provided is from AG news. Please follow the steps below: • (30pts) Data Preprocessing: – (10 pts) Fill the functions in the jupyter notebook to remove punctuation, urls, and numbers. Change text to lower case. – (5 pts) Tokenize input text into a list of tokens. Remove stopwords. – (5 pts) Split data into training, validation, and test sets. – (10 pts) Feature extraction: build your TF-IDF feature extractor for the provodied dataset. • (60pts) Build a logistic regression classifier. – (10 pts) Given the objective function of a logistic regression (LR) model with L2 regularization: J = − 1 N N∑ i=1 K∑ k=1 yik log ( exp fk∑K c=1 exp fc ) + λ d∑ j=1 w2kj (1) Derive the gradient of the objective function of LR with respect to wk. Please write down detailed steps. – (20 pts) Implement this Logistic Regression model. This step includes writing code for initial- ization, objective function, gradient, and gradient descent. – (15 pts) Stochastic Gradient Descent (SGD): fill the code for the function of SGD. – (15 pts) Mini-batch Gradient Descent: fill the code for the function of mini-batch GD. – (0 pts) Evaluation your model with the provided code. 

• (10 pts) Cross-validation: Implement cross-validation on the training data; 

Report the recall and precision for each category on the test and validation sets; 

Choose the best λ using the validation set. Please follow the below instructions when you submit the assignment. 

1. You are NOT allowed to use packages for implementing the code required in this assignment. 

2. Your submission should consist of a zip file named Assignment1 LastName FirstName.zip which contains: 

• a jupyter notebook file(.ipynb). The file should contain the code and the output after execution. You should run all the code provided in the

 jupyteer notebook. You should also include detailed comments. • (optional) a pdf file to show (1) the derivation steps of the gradient of J with 

respect to wk in LR (this can be included in the .ipynb file) and (2) analysis on the results (plots, tables, etc).

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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 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