Learn about Natural Language Processing Machine Learning and the differences in supervised, unsupervised and hybrid machine learning for NLP in this primer.
The sub-branch of Artificial Intelligence (AI) that focuses on facilitating the interaction between humans and machines using natural language is known as Natural Language Processing or NLP. It’s a field that combines computer science, data science, and linguistics. And its goal is to develop systems and applications capable of extracting text information from unstructured data sources, interpreting it, analyzing it, understanding its meaning and implications, then acting on that understanding to perform specific tasks or solve particular problems.
Machine Learning or ML is the branch of artificial intelligence that’s dedicated to creating systems that are capable of learning and drawing inferences from sets of input or training data based on the application of specially designed mathematical formulas or algorithms. These algorithms and training data create a “learning framework” which guides a system as it develops new ways of responding to the relevant input.
Evolution or Maturing of Machine Learning Models
A machine learning model is the mathematical representation of the clean and relevant information that the system is structured to learn from. This includes the sum of all the knowledge that the system has gained from its intake of training data, the new knowledge and insights it gains as input and interactions occur, and more learning occurs.
Machine learning models are typically designed with the ability to generalize and deal with new cases and information. So if a system encounters a situation resembling one of its past experiences, it can use the previous learning it acquired in evaluating the new case. And as the system matures, it can continuously improve, evolving and adapting to fresh input.
Language is continuously evolving, with new expressions, abbreviations, and usage patterns emerging in response to changing social, economic, and political conditions. The data sets that NLP systems have to deal with are also complex and increasing in volume. For natural language processing machine learning provides a logical framework for data handling and the tools and flexibility needed for dealing with a complex and demanding discipline.
Machine Learning for NLP
The statistical mechanisms employed in text analytics and machine learning for natural language processing are designed to identify parts of speech, text entities, the sentiment expressed in language, and other factors.
Supervised Learning for Natural Language Processing
Statistical techniques for machine learning may be expressed in the form of a model that can be applied to other data. This is known as supervised learning, and for natural language processing and text analytics, a set of text documents are typically annotated or “tagged” to display examples of what the system should look for and how it should interpret each aspect. This set of reference documents is the basis for training a supervised learning model. After this initial training, the system is usually given raw or untagged information to analyze. To improve the model over time, larger or more detailed data sets may be used for retraining.
Algorithms for supervised machine learning are typically guided (supervised) by a human data scientist. Some of the most popular algorithms include Bayesian Networks, Conditional Random Field, Support Vector Machines, and Deep Learning or Neural Networks.
Several techniques are typically employed in supervised learning for NLP. They include the following:
Tokenization
Tokenization is the process of splitting up a text document into smaller units or tokens, which a machine can more easily recognize and handle.
Machine learning plays an essential part in tokenization — particularly in languages like Mandarin Chinese, which have no white space between different words. For logographic languages like this, you can train a machine learning model to identify and understand the syntax structure rules.
Part of Speech (PoS) Tagging
In Part of Speech or PoS tagging, nouns, adjectives, adverbs, and other parts of speech in a document token may be identified and annotated or tagged. Several natural language processing tasks rely on Part of Speech tagging. These include recognizing text entities, extracting themes from a body of text, and processing sentiment.
Named Entity Recognition
A simple named entity is a person, place, or object that’s mentioned in a text document. More complex entities include email addresses, phone numbers, Twitter handles, and hashtags.
Supervised machine learning for named entity recognition typically involves extensive training of models on large bodies of previously tagged entities. So successful models for Named Entity Recognition also rely on accurate Part of Speech tagging.
Sentiment Analysis
Establishing the nature of the opinions expressed in a piece of text or commentary is now a critical part of marketing and customer relationship management across various industries and throughout the social media landscape. Sentiment analysis is a natural language processing technique, which makes this possible.
In sentiment analysis, natural language processing machine learning algorithms can determine whether a particular piece of commentary is positive, negative, or neutral. The models also assign a weighted sentiment score to each theme, subject, entity, or category within a document.
Context varies wildly between documents and platforms, so it’s necessary to create a specific set of natural language processing rules for each particular sentiment analysis use case. This task can be made easier by using previously scored data from similar applications.
Categorization and Classification
Categorization of natural language data provides an overview of the available information by sorting content into set categories according to various criteria. Pre-categorized information may then be used to provide the basis for data scientists to train a text classification model for supervised learning. They can then tweak this model until it achieves the desired level of accuracy.
Unsupervised Machine Learning for Natural Language Processing
In unsupervised machine learning, the data employed in training a model is not annotated or tagged. The process typically involves a set of algorithms that operate across large sets of information to extract meaning. By minimizing or eliminating human intervention in the machine learning process, unsupervised learning tends to be less labor-intensive. As with supervised learning, several techniques may be involved.
Clustering
In clustering for unsupervised learning, several similar documents are arranged or clustered together into sets or groups. Hierarchical classification is then applied to sort the clusters based on their importance or relevance.
Latent Semantic Indexing (LSI)
In Latent Semantic Indexing (LSI), algorithms for unsupervised machine learning work to identify words and phrases which frequently occur with each other. Data scientists typically use Latent Semantic Indexing to return search engine results that aren’t necessarily based on the exact search phrase entered or to conduct more intricate searches based on different aspects of a particular subject.
Matrix Factorization
Matrix Factorization is an unsupervised learning technique that uses variables known as latent factors to break a large matrix down into a combination of two smaller matrices. The latent factors typically identify similarities between the data items in a matrix.
Hybrid Machine Learning Systems for Natural Language Processing
It’s possible to perform language analysis via a rules-based approach by setting up a system of parameters that a computer should use when analyzing text. In some cases, this can be a helpful supplement to machine learning models, which have their limitations.
Specifically, machine learning for NLP is excellent at recognizing text entities and the overall sentiment of a document. However, machine learning models may experience difficulty in extracting themes and topics from a mass of text. They also have limited success in matching sentiment to individual entities or themes.
These obstacles may be overcome by combining supervised and unsupervised machine learning with a set of specially formulated rules and patterns.
In combination with a set of rules, machine learning can perform low-level text functions like tokenization, transforming unstructured text into structured data. For mid-level functions such as extracting the author’s identity of a piece of text and the content and subject of what they are saying, machine learning alone may be enough. However, the introduction of rules and patterns can improve performance. And for higher-level sentiment analysis, a combination of machine learning and rules set in NLP code can provide a more nuanced and accurate assessment.
