Demystifying NLP Algorithms: A Comprehensive Guide

Natural language processing (NLP) algorithms allow artificial intelligence (AI) programs to understand, and respond to, human language. NLP algorithms input massive amounts of human-derived data—emails, videos, voice messages, etc.—and identify patterns in it. From there, an AI model can output factually accurate, human-like replies to queries or requests. 

NLP algorithms are important for supporting a variety of applications. They: 

• Power customer service chatbots

• Can archive and process large stores of data

• Give answers to customer queries in a human-like way

Understanding NLP algorithms

NLP algorithms program computers to understand human communication via: 

• Statistical modeling: The analysis of data to make accurate predictions based on said data.

• Machine learning: The use of data and training algorithms that allow machines to learn like humans do.

• Deep learning: A machine learning concept whereby a computer learns via neural networks that resemble those of the human brain. 

• Computational linguistics: A method of analyzing the structure of human speech so that computers can understand not just words but sentiment, implication, and other sophisticated elements endemic to human communication. 

Programmers use machine learning, deep learning, and statistical modeling in tandem to train an NLP model. Computational linguistics allows programmers to understand complex machine language learning in terms of two basic concepts: 

• Syntactical analysis: The way an NLP algorithm understands individual words and their grammatical placement in a sentence.

• Semantic analysis: How an NLP algorithm understands the overall meaning of a sentence once it has parsed it syntactically. 

How NLP algorithms learn

NLP algorithms don’t “learn” language in the holistic, experiential way humans do. Rather, they approximate human learning by parsing and sorting data. 

One step in that process is tokenization. Via tokenization, an NLP algorithm categorizes the words (but not the punctuation) of a sentence into individual semantic units, or tokens. This allows the algorithm to recognize group similarities among individual words—and part-of-speech tagging is how the algorithm then learns to classify nouns with nouns, prepositions with prepositions, and so on. 

Named entity recognition further automates the classification of certain parts of speech such as: 

• Names

• Proper nouns

• Expressions of time

• Expressions of quantity

• Medical language

Topic modeling is how an NLP algorithm classifies words and concepts in previously unstructured data sets into related topics. This teaches an NLP algorithm that certain words are likely to cluster together in human speech because of their related topicality—medical terms with other medical terms, for example. 

Sentiment analysis is how AI understands the tone of voice in a piece of text and matches its output to it. Sentiment analysis is the means by which an NLP algorithm appears to understand how customers feel about a topic or product, and, in a chatbot scenario, how it responds to customers’ emotions in kind. 

Common NLP algorithms

Three main categories of NLP algorithms exist:

Statistical algorithms

Statistical algorithms involve inputting large amounts of unstructured data into a training framework. Your NLP learns to notice language patterns in that data based on the statistical likelihood of one word following another. You can think of statistical algorithms as very complex autocorrect functions. 

Statistical algorithms excel at speech recognition, machine translation, and sentiment analysis, but don’t teach the subtleties of language as well as symbolic algorithms. 

Symbolic algorithms

Symbolic algorithms inhere on analyzing the meaning of words and the logical relationships between them. 

Symbolic algorithms teach an AI to understand context and ambiguity by having it conceptualize words and sort them by related concepts. In an effort to optimize symbolic NLP algorithms, subject matter experts sometimes manually correct machine learning errors so that their NLP can more accurately categorize terms. 

Hybrid algorithms

As the name suggests, hybrid algorithms put to use both symbolic and statistical NLP algorithms. You can build one type of algorithm atop another, or use them in a parallel fashion. 

Indeed, the pair work symbiotically: statistical algorithms can parse enormous stores of data that symbolic algorithms have trouble handling at scale, while symbolic algorithms can understand subtleties within that data that are beyond the capabilities of statistical algorithms. 

Sentiment analysis and NLP algorithms

You can find three main approaches to sentiment analysis with regard to NLP algorithms: 

Rule-based

Rule-based sentiment analysis assigns scores to different words in a sentiment lexicon—the emotion-based words list programmed into a machine learning model. For instance, you can assign to the word “enjoy” a score of 10, and assign to “hate” a score of -10. 

Add these up to score your sentence, paragraph, or passage, which you analyze against a predetermined sentiment threshold. If it exceeds the threshold, the sentiment is positive; if it dips below it, it’s negative. 

Machine learning

Machine learning-based sentiment analysis trains an NLP on a large number of pre-programmed examples. A machine learning algorithm learns to group like sentiments together—positive with positive, negative with negative—and applies that learning to newly input data. 

This approach requires quite a lot of data to be input, which is standard for machine learning models. 

Hybrid

The hybrid method unites both rule-based and machine learning algorithms to more thoroughly teach natural language to a computer, which can then output more sophisticated responses. 

Deep learning in NLP

Traditional machine learning techniques, such as logical regression and naive Bayes rely on statistical analysis and probability-based output. But deep learning-based NLP algorithms exist, too. 

Convolutional neural network (CNN)

A CNN allows an NLP algorithm to classify, analyze, and recognize data. It’s especially good for analyzing images and audio inputs. 

CNNs further allow an NLP to analyze image inputs and produce outputs—to take action or make recommendations. This is how NLP algorithms used in self-driving cars learn lane recognition and how to re-center a vehicle, or how algorithms programmed into radiological systems identify cancer cells that radiologists can miss. 

Recurrent neural network (RNN)

RNNs train on sequential inputs; from there, they learn to predict sequentially appropriate outputs. 

You can teach an RNN to, say, predict weather patterns based on previously input weather patterns for the same time period the year prior. RNNs are also good for: 

• Language translation

• Image captioning

• Speech recognition

RNNs are capable of these functions due to long short-term memory (LSTM) algorithms, which can learn long-term dependencies. 

Transformer models

Transformer models make it easier for an NLP to take input data and “transform” it into something intelligible in terms of output. Basically, a transformer model allows an NLP to make more sense of sequential data via the concept of attention—weighting certain inputs as more or less likely to follow each other in logical sequence. This gives transformer models a predictive power that RNNs lack. The transformer model improves NLP. Two major transformer models include: 

• A generative pre-trained transformer (GPT—as in ChatGPT), which manages to be so accurate and scalable because it processes huge amounts of input data, some of it sequential, all at once, rather than just sequentially as it’s introduced into the algorithm. A GPT therefore understands both individual words and their predictions and weighted sequential data. 

• A bidirectional encoder representations from transformer (BERT), which reads text sequences both right-to-left and left-to-right in order to fully understand each individual word’s context within a sentence. In this way, BERTs allow for better prediction by teaching an NLP what words commonly come not only after but also before a certain word. 

Challenges and future directions

As far as NLP algorithms have come, challenges remain. These include: 

Ambiguity 

Human language is profoundly complex, and NLP algorithms just aren’t there yet. They continue to have trouble with subtlety, context, and the way some words have multiple meanings; they also have trouble understanding metaphors, idioms, cultural context, and slang. 

Some programmers attempt to solve certain NLP algorithm difficulties via multimodal learning. This is a form of machine training that relies on not just text but also video, audio, and image inputs. The hope is that inputting sensory-diverse data will teach an NLP algorithm to understand the various contexts and subtleties of human language in a more sophisticated way. 

Bias 

NLP algorithms are only as good as the data they’re trained on. And if said data is bias-ridden, an AI may output racist, sexist, or in some other way biased content. This becomes a concern with regard to AI-based surveillance technology, for instance, or any other technology that could disproportionately affect those against whom your AI model is biased. 

Translation 

Languages aren’t all structured the same. That means that an NLP algorithm built on certain grammatical, syntactic, and word-choice assumptions normative in one language would not necessarily be able to accurately process data in a different language. 

Attempts to solve this problem involve cross-lingual understanding, whereby you train an NLP to recognize semantic similarities between languages. A difficulty here is that unsupervised training isn’t yet viable due to the complex variations between languages already mentioned; regular human intervention is required. 

Interpretability

NLP interpretability—how transparently understandable and modifiable a machine learning model is—decreases as the amount of data you train an NLP algorithm on increases. As an NLP becomes more complex, it becomes difficult to unravel its input data, making it hard to make alterations to, say, biased data, or to track down the sources of specific training mistakes. 

In other words, while increased complexity may make NLPs smarter, it also makes them less trustworthy, reliable, and fixable. 

Some see a solution in few-shot learning. Few-shot learning is a training model in which you use very small data sets to train a predictive AI model. This method shows promise for the future of NLP training: Programmers can adapt extant large language models—trained on huge data sets—to specific tasks such as sentiment analysis, resulting in NLP sophistication without requiring the continuous, unsupervised input of convoluted data sets. 

Learning more about NLP algorithms

Programmers continue to experiment with NLP algorithms, hoping to create the most human-like possible learning and output scenarios. If you’re interested in joining them, look on Coursera. 

DeepLearning.AI offers a Natural Language Processing Specialization course as well as Natural Language Processing Specialization with Probabilistic Models. Discover more about NLP algorithms and other exciting developments in machine learning and artificial intelligence.