Augmentations in NLP

Data Augmentation techniques in NLP show substantial improvements on datasets with less than 500 observations, as illustrated by the original paper.

https://arxiv.org/abs/1901.11196

The Paper Considered here is EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

/kaggle/input/tweet-sentiment-extraction/train.csv
/kaggle/input/tweet-sentiment-extraction/test.csv
/kaggle/input/tweet-sentiment-extraction/sample_submission.csv

!pip install jovian --upgrade --quiet

WARNING: You are using pip version 20.2.2; however, version 20.2.4 is available.
You should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.

import jovian
project_name = "eda-data-augmentation-techniques-for-text-nlp"

Simple Data Augmentatons Techniques are:

SR : Synonym Replacement
RD : Random Deletion
RS : Random Swap
RI : Random Insertion