instruction:
In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.
question:
We conducted an experiment to determine whether we could maintain or improve classifier performances by applying the following three-tiered feature elimination approach:

Reduction We reduced the dataset encoded for each class by eliminating features that occur less than twice in the full dataset.

Selection We iteratively applied Chi-Square feature selection on the reduced dataset, selecting the top percentile of highest ranked features in increments of 5 percent to train and test the support vector model using a linear kernel and 5-fold, stratified cross-validation.

Rank We cumulatively plotted the average F1-score performances of each incrementally added percentile of top ranked features. We report the percentile and count of features resulting in the first occurrence of the highest average F1-score for each class.
Question: What are the three steps to feature elimination?
answer:
Reduction Selection Rank

question:
In this section, we analyze the Hamming distance between the projections of the sentences from the enwik9 dataset and the corresponding projections of the same sentences after applying character level perturbations. We experiment with three types of character level perturbation BIBREF11 and two types of word level perturbation operations.

Perturbation Study ::: Character Level Perturbation Operations
insert(word, n) : We randomly choose n characters from the character vocabulary and insert them at random locations into the input word. We however retain the first and last characters of the word as is. Ex. transformation: $sample
ightarrow samnple$.

swap(word, n): We randomly swap the location of two characters in the word n times. As with the insert operation, we retain the first and last characters of the word as is and only apply the swap operation to the remaining characters. Ex. transformation: $sample
ightarrow sapmle$.

duplicate(word, n): We randomly duplicate a character in the word by n times. Ex. transformation: $sample
ightarrow saample$.

Perturbation Study ::: Character Level Perturbation Operations ::: Word Level Perturbation Operations
drop(sentence, n): We randomly drop n words from the sentence. Ex. transformation: This is a big cat. $
ightarrow $ This is a cat.

duplicate(sentence, n): Similar to duplicate(word, n) above, we randomly duplicate a word in the sentence n times. Ex. transformation: This is a big cat. $
ightarrow $ This is a big big cat.

swap(sentence, n): Similar to swap(word, n), we randomly swap the location of two words in the sentence n times. Ex. transformation: This is a big cat. $
ightarrow $ This cat is big.
Question: How does their perturbation algorihm work?
answer:
same sentences after applying character level perturbations

question:
We first experiment with a suite of non-neural models, including Support Vector Machines (SVMs), logistic regression, Naive Bayes, Perceptron, and decision trees. We finally experiment with neural models, although our dataset is relatively small. We train both a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21) with parallel filters of size 2 and 3, as these have been shown to be effective in the literature on emotion detection in text (e.g., BIBREF22, BIBREF23).
Question: What supervised methods are used?
answer:
Support Vector Machines (SVMs), logistic regression, Naive Bayes, Perceptron, and decision trees a two-layer bidirectional Gated Recurrent Neural Network (GRNN) BIBREF20 and Convolutional Neural Network (CNN) (as designed in BIBREF21)