Homework 1 Solution

This homework will involve conceptual exercises and coding assignments. Instructions below detail how to turn in the conceptual part on Gradescope and codes via turnin.

    • Coding Assignment

In this section you will implement logistic regression and multi-layer neural network architecture for a multi-class classi cation problem. You must implement both of the classi ers from scratch in Python 3, no built in functions from libraries like sklearn can be used. However, you can use libraries like pandas, numpy etc for pre-processing and implementation purpose.

1.1    Problem

Identi cation of political framing is an interesting and important problem in Natural Language Pro-cessing. In simple words, framing means the perspective from which an event is described. For example, the mass immigration issue may be described from an economic perspective (job unavailabil-ity for the natives, increase in house rent and so on) or from a humanitarian perspective (low quality of life of the immigrants, lower wage and so on). Identifying the framing e ect in text is an important problem as framing is often related to the ideological stance of the user. In this assignment you will identify frames in the Tweets of US politicians.


1.2    Dataset Description

You are provided with 1230 Tweets from US politicians where each Tweet is annotated with one of the 17 frames identi ed by political scientists. The detailed description of the frames can be found in Table 2 of the paper "Leveraging Behavioral and Social Information for Weakly Supervised Collective Classi cation of Political Discourse on Twitter"1. The training set can be found in train.csv. You are given the test set of 820 Tweets in the le test.csv. Note that, the gold labels are not provided for the test set. The description of each column of the csv les are given below.

tweet id: Unique identi er for each tweet.

issue: The issue being discussed in the tweet.

text: Text of the tweet.

author: The political identity (Democrat/Republican) of the author of the tweet.

label: Framing label of the tweet (One of the 17 frames). This  eld is kept empty in test.csv.


You have to build classi ers using the training set and predict the labels of the test set. You must not use the test set while training the models. You are provided with a le main.py where you will nd the starter codes. Feel free to de ne any additional functions needed in this le.


    • https://www.aclweb.org/anthology/P17-1069.pdf



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Hint: Politicians from di erent parties often use di erent frames while discussing the same issue to establish their stances. So, knowing the issue being discussed and the political a liation of the politician who is talking may be a good indicator of the frame present in the text.

1.3    Task 1: Logistic Regression (20 Points)

Build a Logistic Regression classi er to identify the frames present in the Tweets. Fill in the function LR() in main.py. This function should learn a Logistic Regression classi er using the training set and predict the frames in the Tweets contained in test.csv. It will output a csv le named test lg.csv with the predicted frames in the tweets in test.csv. test lg.csv must have all the columns in the given order - tweet id, issue, text, author, label. Make sure you preserve the mapping tweet id : text : label, as tweet ids are the identi ers which will be used to match your predicted labels with gold labels. (Hint: Follow the starter code provided)


1.4    Task 2: Multi-layer Neural Network (20 Points)

Build a Multi-layer Neural Network classi er to identify the frames present in the Tweets. Fill in the function NN() in main.py. This function should learn a Multi-layer Neural Network classi er using the training set and predict the frames in the Tweets contained in test.csv. It will output a csv le named test nn.csv with the predicted frames in the tweets in test.csv. test nn.csv must have all the columns in the given order - tweet id, issue, text, author, label. Make sure you preserve the mapping tweet id : text : label, as tweet ids are the identi ers which will be used to match your predicted labels with gold labels. (Hint: Follow the starter code provided)


1.5    Bonus (5 Points at Most)

You must do cross validation and can do any kind of feature engineering to implement the tasks and improve the performance. The top 5 students will be given bonus points which can be supplemented to use any point loss only in the homeworks. The top scorer will get 5 points, 2nd top scorer will get 4, 3rd will get 3 and so on. The evaluation criteria is Macro F1 score of the prediction task on the test set.


























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    • Conceptual Questions (20 Points)

Now answer the following questions based on your implementation of Logistic Regression and Neural Network classi ers.

    1. Write down the logistic function and loss function you used in Logistic Regression and derive the gradient. (4 Points)

    2. Describe the architecture of the neural network you implemented using a computation graph (Must be drawn using any software. Handwritten/drawn ones will be discarded from grad-ing). Your answer should include the activation functions used in each layer (2 Points), the number of layers and number of neurons in each layer (2 Points). State the loss function used in your neural network implementation (2 Points). Derive the partial derivative associated with each node in the computation graph. (4 Points)

    3. State one hyper-parameter you tuned in case of Logistic Regression and one in case of Neural Network. How did tuning these hyper-parameters change the result? Explain with learning curves. You may have tuned more than one hyper-parameter for each model. In this section explain only one from each model. (4 Points)

    4. Explain in three sentences. What is framing? What can this analysis be used for in real-world situations? (2 Points)







































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2.1    Submission Instructions:

2.1.1    Conceptual Part

Upload your answers to the conceptual questions as a typed pdf in gradescope: https://www. gradescope.com/courses/61435

For your pdf le, use the naming convention username hw#.pdf. For example, your TA with username roy98 would name his pdf le for HW1 as roy98 hw1.pdf.

To make grading easier, please start a new page in your pdf le for each question. Hint: use a nnewpage command in LaTeX after every question ends. For example, for HW1, use a nnewpage command after each of the questions 1-4.

After uploading to gradescope, mark each page to identify which question is answered on the page. (Gradescope will facilitate this.)

Follow the above convention and instruction for future homeworks as well.


2.1.2    Coding Part

You need to submit your codes via Turnin. Log into data.cs.purdue.edu (physically go to the lab or use ssh remotely) and follow these steps:

Place only the les main.py, test lr.csv, test nn.csv in a folder named username hw#. For example, your TA with username roy98 would name his folder for HW1 as roy98 hw1. This naming convention is important. If the folder is not named correctly, there’s no way to identify whose submission is that. Hence, may result in no grading.

Change directory to outside of username hw# folder (run cd .. from inside username hw# folder)

Execute the following command to turnin your code: turnin -c cs577 -p hw1 username hw# To overwrite an old submission, simply execute this command again.

To verify the contents of your submission, execute this command: turnin -v -c cs577 -p hw1.

Do not forget the -v option, else your submission will be overwritten with an empty submission.




















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