Machine Learning and Deep Machine Learning Algorithms.
Table of Contents
Top machine learning algorithms for NLP
o 1. Support Vector Machines (SVM)
o 2. Naive Bayes
o 3. Logistic regression
o 4. Decision trees
o 5. Random forests
o 6. K-nearest neighbours
o 7. Gradient boosting
Top deep machine learning algorithms for NLP
o 1. Convolutional Neural Networks (CNNs)
o 2. Recurrent Neural Networks (RNNs)
o 4. Long Short-Term Memory (LSTM) Networks
o 5. Transformer networks
o 6. Gated Recurrent Units (GRUs)
o 7. Deep Belief Networks (DBNs)
o 8. Generative Adversarial Networks (GANs)
Closing thoughts on NLP machine learning algorithms
Understanding the differences between the algorithms in this list will hopefully help you choose
the correct algorithm for your problem. However, we realise this remains challenging as the choice
will highly depend on the data and the problem you are trying to solve. If you remain unsure, try
a few out to see how they perform.
Solving NLP problems requires specific machine learning algorithms.
Top machine learning algorithms for NLP
Many different machine learning algorithms can be used for natural language processing (NLP).
But to use them, the input data must first be transformed into a numerical representation that the
algorithm can process. This process is known as “preprocessing.” See our article on the most
common preprocessing techniques for how to do this. Also, check out preprocessing in Arabic if
you are dealing with a different language other than English.
Once the input data has been turned into a numerical format, the following algorithms can be used:
1. Support Vector Machines (SVM)
In natural language processing (NLP), SVMs can classify text documents or predict labels for
words or phrases.
The SVM algorithm finds the hyperplane in the high-dimensional space that maximally separates
the different classes. The SVM algorithm uses an optimization function to find the hyperplane that
maximizes the margin between the classes.
SVMs are known for their excellent generalisation performance and can be adequate for NLP
tasks, mainly when the data is linearly separable. However, they can be sensitive to the choice of
kernel function and may not perform well on data that is not linearly separable.
2. Naive Bayes
Naive Bayes is a probabilistic classifier commonly used for natural language processing (NLP)
tasks, such as text classification and spam filtering. It is based on the idea that Bayes’ theorem can
be used to figure out how likely it’s a particular class is based on some features.
The Naive Bayes algorithm then works by calculating the probability of each class given the input
features and selecting the class with the highest probability as the prediction. One of the key
assumptions of the Naive Bayes algorithm is that the features are independent of one another,
which is why it is called “naive.”
Naive Bayes is a fast and simple algorithm that is easy to implement and often performs well on
NLP tasks. But it can be sensitive to rare words and may not work as well on data with many
dimensions.
3. Logistic regression
Logistic regression is a supervised machine learning algorithm commonly used for classification
tasks, including in natural language processing (NLP). It works by predicting the probability of an
event occurring based on the relationship between one or more independent variables and a
dependent variable.
The logistic regression algorithm then works by using an optimization function to find the
coefficients for each feature that maximizes the observed data’s likelihood. The prediction is made
by applying the logistic function to the sum of the weighted features. This gives a value between
0 and 1 that can be interpreted as the chance of the event happening.
Logistic regression is a fast and simple algorithm that is easy to implement and often performs
well on NLP tasks. But it can be sensitive to outliers and may not work as well with data with
many dimensions.
4. Decision trees
Decision trees are a type of supervised machine learning algorithm that can be used for
classification and regression tasks, including in natural language processing (NLP). They work by
creating a tree-like decision model based on data features.
The decision tree algorithm splits the data into smaller subsets based on the essential features. This
process is repeated until the tree is fully grown, and the final tree can be used to make predictions
by following the branches of the tree to a leaf node.
Decision trees are simple and easy to understand and can handle numerical and categorical data.
However, they can be prone to overfitting and may not perform as well on data with high
dimensionality.
5. Random forests
Random forests are an ensemble learning method that combines multiple decision trees to make
more accurate predictions. They are commonly used for natural language processing (NLP) tasks,
such as text classification and sentiment analysis.
The random forest algorithm works by training multiple decision trees on random subsets of the
data and then averaging the predictions made by each tree. This process helps reduce the variance
of the model and can lead to improved performance on the test data.
Random forests are simple to implement and can handle numerical and categorical data. They are
also resistant to overfitting and can handle high-dimensional data well. However, they can be
slower to train and predict than some other machine learning algorithms.
6. K-nearest neighbours
K-nearest neighbours (k-NN) is a type of supervised machine learning algorithm that can be used
for classification and regression tasks. In natural language processing (NLP), k-NN can classify
text documents or predict labels for words or phrases.
The k-NN algorithm works by finding the k-nearest neighbours of a given sample in the feature
space and using the class labels of those neighbours to make a prediction. The distance between
samples is typically calculated using a distance metric such as Euclidean distance.
k-NN is a simple and easy-to-implement algorithm that can handle numerical and categorical data.
However, it can be computationally expensive, particularly for large datasets, and it can be
sensitive to the choice of distance metric.
7. Gradient boosting
Gradient boosting is an ensemble learning method that can be used for classification and regression
tasks, including in natural language processing (NLP). It works by training a series of weak
learners, like decision trees, and then taking an average of their predictions.
The gradient boosting algorithm trains a decision tree on the residual errors of the previous tree in
the sequence. This process is repeated until the desired number of trees is reached, and the final
model is a weighted average of the predictions made by each tree.
Gradient boosting is a powerful and practical algorithm that can achieve state-of-the-art
performance on many NLP tasks. However, it can be sensitive to the choice of hyperparameters
and may require careful tuning to achieve good performance.
Top deep machine learning algorithms for NLP
Deep learning algorithms are a type of machine learning algorithms that is particularly well-suited
for natural language processing (NLP) tasks. Similarly, as with the machine learning models, the
input data must first be transformed into a numerical representation that the algorithm can process.
This can typically be done using word embeddings, sentence embeddings, or character
embeddings.
1. Convolutional Neural Networks (CNNs)
Convolutional neural networks (CNNs) are a type of deep learning algorithm that is particularly
well-suited for natural language processing (NLP) tasks, such as text classification and language
translation. They are designed to process sequential data, such as text, and can learn patterns and
relationships in the data.
The CNN algorithm applies filters to the input data to extract features and can be trained to
recognise patterns and relationships in the data. CNN’s are particularly effective at identifying
local patterns, such as patterns within a sentence or paragraph.
CNNs are powerful and effective algorithms for NLP tasks and have achieved state-of-the-art
performance on many benchmarks. However, they can be computationally expensive to train and
may require much data to achieve good performance.
2. Recurrent Neural Networks (RNNs)
Recurrent neural networks (RNNs) are a type of deep learning algorithm that is particularly well-
suited for natural language processing (NLP) tasks, such as language translation and modelling.
They are designed to process sequential data, such as text, and can learn patterns and relationships
in the data over time.
The RNN algorithm processes the input data through a series of hidden layers, with each layer
processing a different part of the sequence. At each time step, the input and the previous hidden
state are used to update the RNN’s hidden state. This lets the RNN learn patterns and dependencies
in the data over time.
RNNs are powerful and practical algorithms for NLP tasks and have achieved state-of-the-art
performance on many benchmarks. However, they can be challenging to train and may suffer from
the “vanishing gradient problem,” where the gradients of the parameters become very small, and
the model is unable to learn effectively.
4. Long Short-Term Memory (LSTM) Networks
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN)
designed to remember long-term dependencies in the data. They are particularly well-suited for
natural language processing (NLP) tasks, such as language translation and modelling, where
context from earlier words in the sentence is important.
The LSTM algorithm processes the input data through a series of hidden layers, with each layer
processing a different part of the sequence. The hidden state of the LSTM is updated at each time
step based on the input and the previous hidden state, and a set of gates is used to control the flow
of information in and out of the cell state. This allows the LSTM to selectively forget or remember
information from the past, enabling it to learn long-term dependencies in the data.
LSTMs are a powerful and effective algorithm for NLP tasks and have achieved state-of-the-art
performance on many benchmarks. However, they can be computationally expensive to train and
may require much data to perform well.
5. Transformer networks
Transformer networks are a type of deep learning algorithm introduced in the paper “Attention is
All You Need.” They are especially good at natural language processing (NLP) tasks, like
translating and modelling languages, and have reached the top of the field on many NLP
benchmarks.
The Transformer network algorithm uses self-attention mechanisms to process the input
data. Self-attention allows the model to weigh the importance of different parts of the input
sequence, enabling it to learn dependencies between words or characters far apart. This allows the
Transformer to effectively process long sequences without recursion, making it efficient and
scalable.
Transformer networks are powerful and effective algorithms for NLP tasks and have achieved
state-of-the-art performance on many benchmarks. However, they can be computationally
expensive to train and may require much data to perform well.
6. Gated Recurrent Units (GRUs)
Gated recurrent units (GRUs) are a type of recurrent neural network (RNN) that was introduced
as an alternative to long short-term memory (LSTM) networks. They are particularly well-suited
for natural language processing (NLP) tasks, such as language translation and modelling, and have
been used to achieve state-of-the-art performance on some NLP benchmarks.
The GRU algorithm processes the input data through a series of hidden layers, with each layer
processing a different sequence part. The hidden state of the GRU is updated at each time step
based on the input and the previous hidden state, and a set of gates is used to control the flow of
information in and out of the hidden state. This allows the GRU to selectively forget or remember
information from the past, enabling it to learn long-term dependencies in the data.
GRUs are a simple and efficient alternative to LSTM networks and have been shown to perform
well on many NLP tasks. However, they may not be as effective as LSTMs on some tasks,
particularly those that require a longer memory span.
7. Deep Belief Networks (DBNs)
Deep Belief Networks (DBNs) are a type of deep learning algorithm that consists of a stack
of restricted Boltzmann machines (RBMs). They were first used as an unsupervised learning
algorithm but can also be used for supervised learning tasks, such as in natural language processing
(NLP).
The DBN algorithm works by training an RBM on the input data and then using the output of that
RBM as the input for a second RBM, and so on. This process is repeated until the desired number
of layers is reached, and the final DBN can be used for classification or regression tasks by adding
a layer on top of the stack.
DBNs are powerful and practical algorithms for NLP tasks, and they have been used to achieve
state-of-the-art performance on some benchmarks. However, they can be computationally
expensive to train and may require much data to perform well.
8. Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) are a type of deep learning algorithm that can generate
synthetic data similar to a given training dataset. They consist of two neural networks: a generator
network that produces synthetic data and a discriminator network that tries to distinguish between
real and synthetic data.
GANs have been applied to various tasks in natural language processing (NLP), including text
generation, machine translation, and dialogue generation. The input data must first be transformed
into a numerical representation that the algorithm can process to use a GAN for NLP. This can
typically be done using word embeddings or character embeddings.
The GAN algorithm works by training the generator and discriminator networks simultaneously.
The generator network produces synthetic data, and the discriminator network tries to distinguish
between the synthetic and real data from the training dataset. The generator network is trained to
produce indistinguishable data from real data, while the discriminator network is trained to
accurately distinguish between real and synthetic data.
GANs are powerful and practical algorithms for generating synthetic data, and they have been
used to achieve impressive results on NLP tasks. However, they can be challenging to train and
may require much data to achieve good performance.
Closing thoughts on NLP machine learning algorithms
We hope this list of the most popular machine learning algorithms has helped you become more
familiar with what is available so that you can deep dive into a few algorithms and discover them
further.
