Primers • Data Sampling
 Overview
 Application of Sampling in Natural Language Processing
 Random Sampling
 Stratified Sampling
 Hard Sampling (or Hard Negative Mining)
 Negative Sampling
 Diving Deeper: Hard Sampling vs. Negative Sampling
 Citation
Overview

Sampling techniques are foundational methods employed to derive a representative fraction (the sample) from a larger set (the population). The objective is to draw conclusions about the entire population based on the characteristics of the chosen sample. The ideal sampling technique hinges upon the nature of the population and the research’s goal. Below is an indepth exploration of prevalent sampling techniques:

Random Sampling: At its core, random sampling exemplifies true probability sampling. In this technique, every population member stands an equal likelihood of being a part of the sample, ensuring unbiased representation.

Systematic Sampling: Systematic sampling revolves around a systematic approach where units are selected at uniform intervals from an organized population list. The interval, known as the “sampling interval”, is derived by dividing the population size by the desired sample size. An initial point is chosen randomly, and subsequent selections are made at the determined interval.

Stratified Sampling: This method necessitates the division of the population into analogous subgroups, termed ‘strata’. Each stratum is created based on a particular attribute or characteristic. Samples are then drawn from each stratum, either proportionately or uniformly, ensuring a comprehensive representation of all segments of the population.

Cluster Sampling: Here, the population is segmented into multiple clusters, usually based on geographical or organizational divisions. Instead of sampling from each cluster, a random assortment of clusters is chosen, and all entities within these chosen clusters form the sample.

Multistage Sampling: This sophisticated method blends multiple sampling techniques at successive stages. An exemplar scenario might involve first utilizing cluster sampling to choose particular areas within a city and then employing random sampling within those areas to select households.

Quota Sampling: A nonprobability method, quota sampling ensures that the sample mirrors certain attributes or characteristics of the entire population. Researchers set quotas for individual subgroups, and once those quotas are attained, sampling concludes.

Convenience Sampling: As the nomenclature suggests, this nonprobability method depends on the ready availability of members from the population. While convenient, it might not always yield a representative sample.

Snowball Sampling: Predominantly used for studies involving hardtoreach populations, this method depends on existing participants to refer or recruit subsequent participants. It’s particularly useful for studying networks or specific communities.


Each method carries distinct advantages and potential limitations. The technique’s selection is integral to the accuracy and reliability of research findings, and it is contingent on the study’s objectives, the population’s structure, and available resources.
Application of Sampling in Natural Language Processing

Sampling plays a pivotal role in diverse Natural Language Processing (NLP) tasks. Here are enhanced insights into its applications:

Dataset Creation: Crafting a dataset tailored for specific NLP tasks might not always warrant the use of all extant data. Sampling helps cull a representative fraction that mirrors the broader data spectrum.

Addressing Imbalanced Classes: Text classification tasks occasionally grapple with stark class imbalances. Sampling can rectify this, with undersampling curtailing majority class instances or oversampling amplifying minority class instances.

Negative Sampling in Word Embeddings: In tasks such as word2vec, negative sampling is indispensable. The goal here is to sample negative instances (contextword pairs absent in the text) to enrich the learning experience of the model.

Training Efficacy: At times, computational constraints might preclude the use of extensive data for model training. Here, sampling proves instrumental in choosing a data subset without compromising training quality.

Model Evaluation: Posttraining, models undergo rigorous evaluations. Sampling is often employed to draw a subset of the data explicitly for testing and validation purposes.

Random Sampling
 Random sampling, particularly simple random sampling, does not inherently ensure that the original distribution of the population is reflected in the sample. Unlike stratified sampling, which deliberately segments the population to reflect its diversity, simple random sampling is based on pure chance. Here’s why simple random sampling might not always reflect the original distribution:
 Chance Variability: In simple random sampling, every member of the population has an equal chance of being selected. However, this randomness can lead to samples that are not representative, especially in smaller samples. By chance, the sample might overrepresent or underrepresent certain groups within the population.
 Lack of Stratification: Simple random sampling does not take into account the various subgroups or strata that might exist within a population. If the population is heterogeneous (diverse), the sample may not capture this diversity accurately, especially if the sample size is not large enough.
 Sample Size Matters: The accuracy of simple random sampling in reflecting the population distribution improves with larger sample sizes. In smaller samples, the randomness can lead to greater variability and a higher chance of a nonrepresentative sample.
 Potential Bias: If the method of selecting the sample is not truly random (e.g., using a flawed randomization process), there can be biases in the sample that do not accurately reflect the population.
 In contrast, stratified sampling is designed to ensure that all significant subgroups of the population are adequately represented in the sample, thereby better reflecting the original distribution. Random sampling, while useful and easy to implement, may require a larger sample size or additional sampling techniques to achieve a similar level of representativeness.
Stratified Sampling
 Stratified sampling is a technique used to ensure that the sample more accurately reflects the population from which it is drawn, especially when there are significant differences within the population. Here’s how it helps in maintaining the original distribution:
 Division into Strata: The population is divided into different subgroups or strata that are distinct and nonoverlapping. These strata are based on specific characteristics relevant to the research, such as age, income, education, etc.
 Proportional Representation: In each stratum, elements are chosen based on a random sampling method. The key is that the proportion of each stratum in the sample should reflect the proportion of that stratum in the entire population. This ensures that each subgroup is adequately represented in the sample, preserving the original distribution of the population.
 Combining Strata Samples: After sampling from each stratum, the results are combined to form a single sample. This aggregate sample is more representative of the population than a simple random sample would be, especially in cases where certain strata may be underrepresented.
 Stratified sampling can be done with or without replacement:
 Sampling Without Replacement: This is the most common approach. Once an individual or element is selected from a stratum, it is not replaced, meaning it cannot be chosen again. This approach ensures that each member of the population has an equal chance of being selected.
 Sampling With Replacement: In this method, each member of the population can be selected more than once. This is less common in stratified sampling but may be used in certain situations, like when the population size is very small or when a higher degree of randomness is required.
 The choice between sampling with or without replacement depends on the specific goals and constraints of the research. Sampling without replacement is typically preferred to avoid the possibility of the same individual or element being chosen multiple times, which could skew the results.
Hard Sampling (or Hard Negative Mining)
 Hard negative mining is an advanced technique originally used in object detection tasks. It involves selecting the most challenging negative examples—background patches in an image that do not contain the target object but are misclassified by the model as containing it. These “hard negatives” are crucial for training because they help the model learn to differentiate better between true object instances and background noise. By focusing on these difficult cases, the model develops more robust and discriminating features, enhancing its overall accuracy.
 In addition to its application in object detection, hard negative mining has also found utility in various NLP tasks. In tasks like sentiment analysis, entity recognition, or machine translation, hard negatives can be instances where the context or subtleties of language cause the model to misinterpret the input. For example, in sentiment analysis, phrases with nuanced expressions of sentiment may be incorrectly classified due to their complexity or ambiguity. By incorporating these hard negative examples in training, models can be tuned to better handle linguistic nuances, leading to improved accuracy and sensitivity in understanding human language.
 Similarly, in entity recognition, hard negatives might include sentences where entities are mentioned in an unconventional context or obscured by linguistic structures. Training on these hard examples helps the model to more effectively recognize entities across diverse and challenging scenarios.
 In machine translation, hard negatives could be complex sentences that models frequently translate incorrectly. These examples often involve idiomatic expressions, subtle language cues, or complex grammatical structures. Including these in training helps refine the model’s capabilities, enabling it to produce more accurate and contextually appropriate translations.
 Thus, hard negative mining extends well beyond visual tasks, enhancing the sophistication of models across a spectrum of applications in NLP, where understanding context, nuance, and subtle variations in language is crucial for performance.
Negative Sampling
 Negative sampling is a strategic technique utilized predominantly in scenarios featuring highly imbalanced datasets, such as in word2vec training, recommendation systems, or other contexts where negative examples vastly outnumber positive ones. Instead of incorporating all negative examples into the training set, this method involves selecting a small, random subset of negatives for each training iteration. This selective sampling significantly reduces the computational load, accelerates the training process, and still manages to produce models that perform competitively.
 The effectiveness of negative sampling is often attributed to specific loss functions employed to optimize the selection process. For instance, in word2vec, a popular loss function used with negative sampling is the Noise Contrastive Estimation (NCE) loss. NCE aims to distinguish a target word from noise samples (negatives), which are randomly drawn from the vocabulary. By focusing only on a small set of negative samples rather than the entire vocabulary, NCE efficiently trains the model to learn highquality word embeddings.
 Another key loss function is the Margin Ranking Loss (MRL). This loss function encourages the model to increase the distance (or difference in scores) between positive examples and a set of sampled negative examples, enhancing the model’s ability to differentiate between them effectively. MRL is particularly effective in environments like embedding learning and information retrieval, where it is crucial to clearly separate the relevant items from irrelevant ones.
 Adding to these, Triplet Loss is another powerful loss function often used in conjunction with negative sampling, especially in tasks that involve learning finegrained distinctions between similar entities, such as face recognition or person reidentification. Triplet Loss works by taking a set of three items at a time—a positive example, a negative example, and an anchor (usually a different positive example)—and optimizing the model to ensure that the anchor is closer to the positive example than to the negative example by a margin. This helps in creating a precise embedding space where similar examples are clustered together, and dissimilar ones are far apart.
 For tasks that benefit from considering multiple negatives simultaneously, techniques like Multiple Negative Sampling (MNS) can also be employed. MNS incorporates several negative examples for each positive example in the loss function calculation, further enriching the training context and providing a deeper understanding of user preferences in recommendation systems.
 One such instantiation of MNS is the Multiple Negative Ranking Loss (MNRL) which handles multiple negative examples simultaneously. MNRL integrates a broader spectrum of negative samples in each evaluation, allowing for a more complex and nuanced differentiation between positive instances and a variety of negative instances. MNRL is especially useful in deep learning applications such as image and video retrieval or advanced recommender systems, where it promotes finer discrimination between multiple similar yet nonidentical items.
 Overall, negative sampling and its associated loss functions provide a powerful framework for efficiently training machine learning models in situations where dealing with the full set of negative examples would be computationally prohibitive. By smartly sampling negatives and leveraging appropriate loss functions, these techniques help ensure that models remain scalable while still achieving high levels of accuracy and performance.
Diving Deeper: Hard Sampling vs. Negative Sampling
 Hard Sampling (or Hard Negative Mining):
 Originally a technique leveraged in object detection, hard sampling has also become increasingly significant in various NLP tasks. In object detection, the focus is on the most formidable negatives—those incorrectly classified by the model. Integrating these hard negatives into the training process sharpens the model’s ability to distinguish between actual objects and background distractions. This targeted training approach is key to developing finer, more precise feature detection capabilities in the model.
 In NLP, hard negative mining can be used in scenarios like sentiment analysis, where nuanced phrases may be misclassified, or in question answering, where the model might confuse similar but incorrect answers for the right one. Training with these challenging examples allows the model to better understand subtle linguistic cues and complex context, enhancing both its accuracy and sensitivity.
 Negative Sampling:
 Widely employed in NLP and recommendation systems, negative sampling addresses the challenge of a grossly imbalanced dataset by randomly selecting a subset of negative examples for training. This method not only conserves computational resources but also maintains model performance, making it an efficient and effective strategy for dealing with large datasets.
 In NLP, negative sampling is crucial for training models on tasks like language modeling and nextword prediction, where it reduces the overwhelming number of potential negative samples (i.e., incorrect next words) to a manageable size.
 Related: Focal Loss:
 Focal Loss, originally designed for tackling class imbalance in the context of object detection, is another critical enhancement in handling class imbalance. It modifies the standard crossentropy loss such that it downweights the loss assigned to wellclassified examples. This focusing parameter allows models to concentrate more on hard, misclassified examples, which is incredibly beneficial in both hard sampling and tasks with large class imbalances. Focal Loss is especially useful when combined with hard negative mining, as it can further refine the model’s responses to the most challenging and frequently misclassified examples.
 Comparative Analysis:
 While both techniques involve the selection of negative examples, hard sampling concentrates on the most challenging cases to refine detection accuracy. This is particularly useful where precision in feature detection is critical, such as distinguishing subtle facial expressions in images or detecting slight nuances in tone or intent in text. In contrast, negative sampling optimizes computational efficiency by randomly selecting a manageable subset of negatives, which is especially beneficial in scenarios with extremely large datasets.
 Each method has its unique benefits and is critical in different machine learning applications, ensuring a balance between computational efficiency and model effectiveness. By understanding these strategies and their applications, you can choose the most appropriate method depending on the specific needs of your machine learning task, whether it involves improving model precision or managing large datasets efficiently. This insight allows developers and researchers to tailor their approach to the specific challenges and requirements of their projects, leading to more efficient and effective outcomes.
Citation
If you found our work useful, please cite it as:
@article{Chadha2020DistilledDataSampling,
title = {Data Sampling},
author = {Chadha, Aman and Jain, Vinija},
journal = {Distilled AI},
year = {2020},
note = {\url{https://aman.ai}}
}