Data Augmentation | Dagster Glossary

Back to Glossary Index

Data Augmentation

Add new data or information to an existing dataset to enhance its value.

Data augmentation definition:

Data augmentation can be applied to various types of data beyond images, audio, and video. Here's an example of how data augmentation can be used in text data:

Suppose you have a dataset of text reviews for a product and you want to classify them as positive or negative. However, the dataset is imbalanced with a lot more positive reviews than negative ones. To address this issue, you can use data augmentation techniques to generate synthetic negative reviews from the existing ones.

One technique is to use synonym replacement where you replace certain words in the negative reviews with their synonyms. For example, you can replace the word "bad" with "poor" or "terrible". This generates new negative reviews that have a slightly different wording but still convey the same sentiment.

Synonym replacement in Python using NLTK

Here's an example of how to implement synonym replacement using the NLTK library in Python (which you can install with pip install nltk):

Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

import nltk
import random
nltk.download('punkt')
nltk.download('wordnet')
from nltk.corpus import wordnet

# Define a sentence to modify
sentence = "The quick brown fox jumps over the lazy dog"

# Tokenize the sentence into words
words = nltk.word_tokenize(sentence)

# Loop through each word in the sentence
for i, word in enumerate(words):
   # Get the synonyms for the word
   synonyms = []
   for syn in wordnet.synsets(word):
       for lemma in syn.lemmas():
           synonyms.append(lemma.name())
   # Replace the word with a random synonym, if available
   if len(synonyms) > 0:
       words[i] = synonyms[random.randint(0, len(synonyms)-1)]

# Join the modified words back into a sentence
new_sentence = " ".join(words)

# Print the original and modified sentences
print("Original sentence:", sentence)
print("Modified sentence:", new_sentence)

This function takes in a text string and the number of synonym replacements to perform (default is 5). It uses the NLTK library to get synonyms for each word in the text and replaces them with a randomly chosen synonym. The function returns the augmented text with the specified number of replacements.

Your output might look something like this:

Original sentence: The quick brown fox jumps over the lazy dog
Modified sentence: The quick dark-brown fob jump o'er the slothful dog

Although it may equally give you this:

Modified sentence: The flying Brown_University Fox jump-start all_over the faineant chase

…which is a bit weird. While NLTK's WordNet is a useful resource for obtaining synonyms, it may sometimes return seemingly unrelated or nonsensical words. To avoid this, you can try the following approaches:

  1. Filter by part of speech (POS): WordNet provides synonyms for different parts of speech like nouns, verbs, adjectives, and adverbs. By specifying the part of speech, you can get more relevant synonyms.

  2. Use the most common sense: Each synonym in WordNet has a sense number, which indicates its commonness. To avoid nonsensical synonyms, you can choose only the most common sense or limit the number of senses you consider.

  3. Context-based filtering: If you have some context for the word you're finding synonyms for, you can use that context to filter out irrelevant synonyms. You can use a pre-trained language model like BERT, GPT, or ELMo to rank the synonyms based on their contextual fit.

  4. Cosine similarity: Calculate the cosine similarity between the word embeddings of the original word and its synonyms. By selecting the synonyms with the highest similarity scores, you can get more relevant results. You can use pre-trained embeddings like Word2Vec, GloVe, or FastText for this purpose.

  5. Custom filtering rules: Create your own filtering rules based on your specific use case. For example, if you're only interested in synonyms of a certain length, you can filter out shorter or longer synonyms.

  6. Use a more advanced thesaurus: If you find WordNet lacking, you can try other resources like BabelNet, which combines WordNet with other linguistic resources to provide a richer set of synonyms and translations.

  7. Manual review: As a last resort, you can manually review the list of synonyms and remove any that don't make sense in your specific use case. This might be time-consuming but can help ensure the quality of your results.

Remember that no automated synonym extraction method is perfect, so using a combination of these approaches may yield the best results.

An example of simple character level noise injection

In this example, we will perform data augmentation using a simple character-level random noise injection technique. This method is particularly useful for augmenting text data, especially when training models that need to be robust to noisy inputs.

Here's a Python implementation of the random noise injection data augmentation technique:

import random
import string

def inject_noise(text, noise_level=0.1):
    """
    Injects random noise into the input text at the character level.

    :param text: The input text to be augmented
    :type text: str
    :param noise_level: The proportion of characters to be replaced with random characters (default: 0.1)
    :type noise_level: float
    :return: The augmented text with injected noise
    :rtype: str
    """
    augmented_text = []
    for char in text:
        if random.random() < noise_level:
            random_char = random.choice(string.ascii_letters)
            augmented_text.append(random_char)
        else:
            augmented_text.append(char)
    return "".join(augmented_text)

# Example usage
input_text = "The quick brown fox jumps over the lazy dog."
augmented_text = inject_noise(input_text, noise_level=0.1)
print(f"Original text: {input_text}")
print(f"Augmented text: {augmented_text}")

In this example, the inject_noise function takes a text string as input and replaces a proportion of the characters with random characters, based on the specified noise_level. The function then returns the augmented text. This simple method can help create a more diverse and robust training dataset for various NLP tasks.


Other data engineering terms related to
Data Management:
Dagster Glossary code icon

Append

Adding or attaching new records or data items to the end of an existing dataset, database table, file, or list.
An image representing the data engineering concept of 'Append'

Archive

Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. Store data for long-term retention and compliance.
An image representing the data engineering concept of 'Archive'

Backup

Create a copy of data to protect against loss or corruption.
An image representing the data engineering concept of 'Backup'
Dagster Glossary code icon

Batch Processing

Process large volumes of data all at once in a single operation or batch.
An image representing the data engineering concept of 'Batch Processing'
Dagster Glossary code icon

Cache

Store expensive computation results so they can be reused, not recomputed.
An image representing the data engineering concept of 'Cache'
Dagster Glossary code icon

Categorize

Organizing and classifying data into different categories, groups, or segments.
An image representing the data engineering concept of 'Categorize'
Dagster Glossary code icon

Deduplicate

Identify and remove duplicate records or entries to improve data quality.
An image representing the data engineering concept of 'Deduplicate'

Deserialize

Deserialization is essentially the reverse process of serialization. See: 'Serialize'.
An image representing the data engineering concept of 'Deserialize'
Dagster Glossary code icon

Dimensionality

Analyzing the number of features or attributes in the data to improve performance.
An image representing the data engineering concept of 'Dimensionality'
Dagster Glossary code icon

Encapsulate

The bundling of data with the methods that operate on that data.
An image representing the data engineering concept of 'Encapsulate'
Dagster Glossary code icon

Enrich

Enhance data with additional information from external sources.
An image representing the data engineering concept of 'Enrich'

Export

Extract data from a system for use in another system or application.
An image representing the data engineering concept of 'Export'
Dagster Glossary code icon

Graph Theory

A powerful tool to model and understand intricate relationships within our data systems.
An image representing the data engineering concept of 'Graph Theory'
Dagster Glossary code icon

Idempotent

An operation that produces the same result each time it is performed.
An image representing the data engineering concept of 'Idempotent'
Dagster Glossary code icon

Index

Create an optimized data structure for fast search and retrieval.
An image representing the data engineering concept of 'Index'
Dagster Glossary code icon

Integrate

Combine data from different sources to create a unified view for analysis or reporting.
An image representing the data engineering concept of 'Integrate'
Dagster Glossary code icon

Lineage

Understand of how data moves through a pipeline, including its origin, transformations, dependencies, and ultimate consumption.
An image representing the data engineering concept of 'Lineage'
Dagster Glossary code icon

Linearizability

Ensure that each individual operation on a distributed system appear to occur instantaneously.
An image representing the data engineering concept of 'Linearizability'
Dagster Glossary code icon

Materialize

Executing a computation and persisting the results into storage.
An image representing the data engineering concept of 'Materialize'
Dagster Glossary code icon

Memoize

Store the results of expensive function calls and reusing them when the same inputs occur again.
An image representing the data engineering concept of 'Memoize'
Dagster Glossary code icon

Merge

Combine data from multiple datasets into a single dataset.
An image representing the data engineering concept of 'Merge'
Dagster Glossary code icon

Model

Create a conceptual representation of data objects.
An image representing the data engineering concept of 'Model'

Monitor

Track data processing metrics and system health to ensure high availability and performance.
An image representing the data engineering concept of 'Monitor'
Dagster Glossary code icon

Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.
An image representing the data engineering concept of 'Named Entity Recognition'
Dagster Glossary code icon

Parse

Interpret and convert data from one format to another.
Dagster Glossary code icon

Partition

Data partitioning is a technique that data engineers and ML engineers use to divide data into smaller subsets for improved performance.
An image representing the data engineering concept of 'Partition'
Dagster Glossary code icon

Prep

Transform your data so it is fit-for-purpose.
An image representing the data engineering concept of 'Prep'
Dagster Glossary code icon

Preprocess

Transform raw data before data analysis or machine learning modeling.
Dagster Glossary code icon

Replicate

Create a copy of data for redundancy or distributed processing.

Scaling

Increasing the capacity or performance of a system to handle more data or traffic.
Dagster Glossary code icon

Schema Inference

Automatically identify the structure of a dataset.
An image representing the data engineering concept of 'Schema Inference'
Dagster Glossary code icon

Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.
Dagster Glossary code icon

Secondary Index

Improve the efficiency of data retrieval in a database or storage system.
An image representing the data engineering concept of 'Secondary Index'

Synchronize

Ensure that data in different systems or databases are in sync and up-to-date.
Dagster Glossary code icon

Validate

Check data for completeness, accuracy, and consistency.
An image representing the data engineering concept of 'Validate'
Dagster Glossary code icon

Version

Maintain a history of changes to data for auditing and tracking purposes.
An image representing the data engineering concept of 'Version'