Understanding Difference Between Machine Learning And NLP

This blog discusses machine learning in natural language processing (NLP) and covers topics such as what is machine learning, key concepts and techniques, applications of NLP, and the difference between machine learning and NLP.

What is Machine learning?

Machine learning in NLP refers to the development of methods that may generate generalizations from examples. Instead of creating a specific method for a task, you create an algorithm that takes a set of labelled examples as input and produces a function or program that can carry out the task on instances that are not visible. In essence, it makes it possible for systems to discover patterns in data on their own without explicit programming. Machine learning is a key component of modern NLP methodologies, and a large portion of current NLP research can be categorized as applied machine learning.

Relationship with Deep Learning

One subfield of machine learning is deep learning. It is occasionally referred to as a rebranded term for the family of learning methods known as neural networks. Through a series of transformations, deep learning techniques learn to accurately describe the data in addition to making predictions. The term “deep learning” refers to the process of connecting numerous layers of differentiable functions.

Learning Paradigms

By using data for training, machine learning techniques can be roughly divided into the following categories:

• In the area of supervised learning, the model is trained using labelled data. The input consists of a collection of instances, each of which has the appropriate output (label) supplied. One category of supervised machine learning methods is neural networks.
• Unsupervised Learning: Unlabeled data is used to train the model. Finding representations, structures, or patterns in the data without direct supervision is the goal. Unsupervised learning is exemplified via clustering.
• Semi-supervised Learning: In this paradigm, training is done using both labelled and unlabeled data.

Key Concepts and Techniques

Information and Display

• A collection of samples with known results that are used to train a machine learning system is called a training corpus or data.
• As the gold standard for assessing the correctness of the trained system, the test set is a subset of the corpus that is kept apart from the training data.
• Validation sets are used to adjust hyperparameters and avoid overfitting during training.
• Converting unprocessed text data into numerical vectors or characteristics that machine learning models can comprehend is known as feature representation or extraction. Among the methods are Distributed Representations (Embeddings), TF-IDF, and Bag of Words (BoW).
• Embeddings are vector-based representations of words or documents that, depending on their context, capture syntactic or semantic characteristics. Word embedding, subword embedding, and document embedding are some examples.

Training and Optimizing Models

• Machine learning models are frequently defined as parameterized functions that receive input, usually a vector, and generate output.
• Learning entails modifying these settings in response to training data.
• Model parameter learning is framed as an optimization issue, frequently involving the minimization of an error or “loss” function.
• Gradient-based Training: By iteratively modifying the parameters in the direction that minimizes the loss, algorithms such as Gradient Descent are frequently employed to determine the ideal parameters.
• Computing Graph: A neural network abstraction that enables backpropagation, or the automatic computing of gradients, for any type of network topology.
• Backpropagation: The neural network gradient computation algorithm that makes training more effective.
• Regularization: To avoid overfitting, which occurs when models perform too well on training data at the price of performance on unseen data, techniques such as Dropout are employed.
• Changing the parameters of the learning process itself, such as the learning rate, batch size, and number of epochs, is known as hyperparameter tuning.

Typical NLP Machine Learning Models and Algorithms

• Classification models are used to give incoming data a distinct label.
• A probabilistic classifier frequently used for text categorization, Naive Bayes is based on Bayes’ theorem. It is regarded as a classifier that is generative.
• A straightforward yet powerful model for binary and multiclass classification is logistic regression. It is a classifier that discriminates.
• SVMs (support vector machines) are useful for classification, especially when there are wide margins.
• Decision Trees: A tree-like model in which a class label is represented by each leaf node, an internal node tests a feature, and a branch indicates the test’s result.
• With just one artificial neuron, the perceptron is the most basic neural network architecture. It functions as a component of more intricate networks. The Average Perceptron and Voting Perceptron are two variations.

Architectures for Deep Learning

• Several layers of artificial neurons form feedforward neural networks (MLPs).
• Sequence labelling and language modelling require architectures like LSTMs and RNNs to process sequential input.
• Convolutional Neural Networks (CNNs): Used in NLP for local feature-based text classification.
• Transformers: A cutting-edge architecture that has taken centre stage, employing attention processes to make it possible for potent models like BERT.
• In unsupervised learning, clustering algorithms are used to put related data points in one group. K-means and Expectation-Maximization (EM)-based algorithms are two examples.
• Probabilistic models for sequence labelling tasks, such as part-of-speech tagging, are called hidden Markov models (HMMs).

Advanced Ideas

• Using a model that has been trained on one job for example, a huge corpus for language modeling as a foundation for training on a different, frequently smaller, downstream task is known as transfer learning.
• The process of further training a pretrained model on a particular downstream job is known as fine-tuning. This frequently entails including task-specific layers.
• Discriminative vs. Generative Models: Discriminative models learn characteristics that aid in class distinction, while generative models construct a model of how data for a class is generated.

Background in Mathematics

Multivariate calculus, linear algebra (including vectors, matrices, derivatives, and partial derivatives), and probability and statistics (including conditional probabilities and independent events) are frequently necessary to comprehend machine learning ideas.

What are the applications of NLP in machine learning?

For many NLP jobs, machine learning is essential, including:

• Text classification (such as spam detection and sentiment analysis).
• Named entity recognition is one kind of information extraction.
• Machine translation.
• Answering questions.
• Both chatbots and dialogue systems.
• Resolution of Coreference.
• Speech Recognition.
• Tagging a portion of speech.
• The disambiguation of word sense.

For various tasks, including converting discrete token sequences, machine learning offers general methods.

Difference between machine learning and NLP