Natural language processing (NLP) is a field that combines computer science, data engineering, and artificial intelligence. While we humans communicate using words, computers rely on the language of numbers. However, it’s possible to use the language of numbers to communicate across linguistic boundaries. We can develop a translation system using NLP to help us communicate more openly and effectively.

Through Natural Language Processing, computers can learn to understand and interpret human language. In fact, NLP is a hot topic in Machine Learning right now. It allows us to “talk” to computers in ways that were considered inconceivable just a few years ago.

Tools like Siri, Alexa, or Google Assistant are just the beginning. 

How Does Natural Language Processing Work?

Through NLP, we humans can communicate with computers without having to translate our questions and commands into computer language since computers have gained the ability to do this on their own. 

Paragraphs and sentences are broken up into linguistic units, and these units are converted into numbers – computer language. In the case of machine translation, these numbers are then converted into another human language like German, French or Italian. This process involves a number of concepts and algorithms. 

Data preprocessing and algorithm development are the two main stages of NLP.

As the name suggests, data preprocessing involves preparing the data so that machine algorithms can analyze and understand it. This can be achieved in a variety of methods, including the following:

• Tokenization: the text is broken down into smaller units;

• Stop word removal: removing some words from the data sample and leaving only the ones that carry the most information and meaning;

• Stemming: reducing words to their stems – their root form

• Lemmatization: reducing words to their lemmas – their most meaningful base form, taking into consideration the morphological analysis of the words;

• Part-of-speech tagging: also called grammatical tagging, tagging words with their corresponding parts of speech such as noun, verb, adjective, or adverb.

When it comes to NLP algorithms, the most extensively used are rules-based or based on machine learning. 

NLP pioneers relied on rules-based systems, which are still in use today. You might wonder why they’re still in use. Since they work by applying carefully designed linguistic rules, they can still be very effective in some situations. 

On the other hand, machine learning algorithms rely on statistical methods and can improve through training. Each data set changes the algorithm a little bit, and it refines its own rules to provide better and better results. 

Natural language processing employs two core strategies: syntax and semantic analysis.

Syntax is the arrangement of words in a phrase in such a way that it makes grammatical sense. In NLP, syntax, and grammar are used to deduce meaning. 

Semantics refers to the meaning and interpretation of words and sentence structure. NLP algorithms use techniques based on semantics to decipher the meaning behind human languages. Here are some examples of techniques based on semantics:

• Word sense disambiguation: one word can have different meanings based on the context. This strategy is concerned with determining which meaning a word has in a particular sentence.

• Named entity recognition: a way of identifying and categorizing crucial pieces of information from unstructured text-based data into predetermined categories such as human names, organizations, and locations. Through a named entity recognition algorithm, a computer can tell the difference between Amazon, the company, and Amazon, the river.

• Natural language generation: using AI to turn data into content in a human language. The computer system can generate new text like a document’s summary or even write a story in natural-sounding sentences and paragraphs.

NLP, as it is now implemented, is based on deep learning, a subset of artificial intelligence that looks for patterns in data and uses those patterns to improve its understanding. Deep learning algorithms require large amounts of labeled data to learn from, and collecting these sets of labeled data is a major NLP challenge. 

Machine Translation

Machine translation refers to translating content using a computer algorithm instead of a human translator. Just like with NLP, the algorithm needs data samples to train and improve. The data sets used during training determine whether the machine translation tool will be generic or specialized. 

Google Translate, one of the most popular machine translation tools right now, is a generic tool designed for the average user. 

There are also specialized machine training engines, usually used by companies and fine-tuned by developers. 

There are many various types of MTs, but here are four of the most common: 

• Rules-based machine translation: Programmers work with language experts to establish grammar rules, semantic patterns, and dictionaries that are incorporated in the algorithms.

• Statistical machine translation: The algorithms go through data samples and form a database of translations sorted by the probability that one word or phrase from language A will correspond to another word or phrase from language B.

• Syntax-based machine translation: This approach is a subtype of statistical machine translation based on the idea of translating syntactic units instead of words.

• Neural machine translation: A machine translation method that employs an artificial neural network to assess the probability of a sequence of words, generally modeling full sentences in a single integrated model. It also leverages statistical machine translation, but the main advantage is that it allows a single system to be trained directly on both source and target language, eliminating the need for a pipeline of specialized systems required in statistical machine learning.

Machine Translation vs. Human Translation

Machine Translation and Human Translation don’t have to compete. They each have their unique advantages. 

Machine translation strikes the optimal balance of cost and speed, allowing brands to quickly translate their documents at scale without incurring significant overhead. But it’s not always the best solution. If the job requires attention to nuance, then you need human translators. This is especially important for branded content that needs to convey the original feeling or message the company intended. 

Human translation may cost a bit more, but it delivers higher-quality results. Machine translation, on the other hand, is ideal for content with a high volume but a low priority, such as user reviews and comments.