How Do Computers Learn to Understand Natural Language?

Arthur Samuel defined machine learning in the 1990s as a computer’s ability to learn on its own without needing additional code. The algorithm learns from its own experiences while processing new data and completing certain tasks. 

Communication is currently one of the most exciting areas in Machine Learning. Natural Language Processing (NLP) now allows humans to communicate with computers in ways that were unthinkable only a few years ago. 

We all use tools like Siri, Alexa and Google Assistant. These tools are just the beginning, though. The main advantage of NLP is that it enables humans to interact with computers without having to translate their queries and instructions into computer language. The computers can do this by themselves. 

NLP technology is especially valuable in data analytics. This technology can structure and analyze unstructured data, providing researchers, business leaders and the general public with insights that allow them to make better-informed decisions. It is already being used by software developers in a variety of applications, including text and document classification, predictive writing, anonymization and machine translation. 

How Does Natural Language Processing Work?

Natural Language Processing has a wide range of applications, but how does it work? It has three essential aspects:

• Voice recognition: Processing human speech and converting the words into text that a machine can read;
• Understanding natural language: The system’s ability to understand human speech;
• Generating natural language: The system’s ability to generate output in the form of natural language that a human being can understand.

The same way we humans have sensors like eyes and ears that allow collecting information about the world around us, computer systems can be equipped with sensors to collect data and be provided with programs that allow them to process and convert that information into something they can understand. 

The two main stages of NLP are data preprocessing and algorithm development. 

Data processing is the stage where the data is prepared so machine algorithms can make sense of it. This can be accomplished in a variety of ways, including:

• Tokenization – the text is split into smaller parts;
• Stop word removal – some words are removed from the data sample, leaving only the words that convey the most information;
• Lemmatization and stemming – words are reduced to their root forms;
• Part-of-speech tagging – words are tagged as nouns, verbs, adjectives and so on. 

The next stage is algorithm development. The two most widely used types of natural language processing algorithms are:

• Rules-based system: This type of algorithms was used in the early stages of NLP and is still in use. It’s based on carefully designed linguistic rules. 
• Machine learning-based system: This type of algorithms relies on statistical methods and improves through training data. The algorithms refine their own rules as they process new sets of data using a combination of machine learning, neural networks and deep learning. 

Syntax and semantic analysis are the two basic strategies employed in natural language processing.

The arranging of words in a phrase to make grammatical sense is known as syntax. NLP analyzes a language’s meaning using syntax and grammatical rules. 

Semantics refers to how words are used and what they mean. Using algorithms, natural language processing can decipher the meaning behind sentences. The following are examples of semantics techniques:

•  Word sense disambiguation: Word sense disambiguation is a technique used to determine the meaning of a word from the context in which it is used. 
•  Named entity recognition: An algorithm using a named entity can differentiate between different entities in a text using semantics and context. For example, it can differentiate between Amazon, the river, and Amazon, the company. 
• Natural language generation: The algorithm can learn from a database and semantics and generate new text such as a document summary. 

Current NLP techniques are based on deep learning, a subset of AI that finds patterns in data and uses those patterns to improve its understanding. Deep learning algorithms require massive sets of labeled data to train on and uncover relevant correlations. Gathering these sets of data is one of the most significant challenges in NLP. 

In the early days of natural language processing, basic machine learning algorithms were given a list of words and phrases to search for, as well as precise responses to those words and phrases. Deep learning offers more flexibility. Algorithms learn to recognize a speaker’s intent from a large number of samples, similar to how a child learns human language.

Computers systems can gain a better understanding of spoken words by combining syntactic and semantic techniques. Natural language is parsed for its validity in terms of formal grammatical rules using syntactic analysis, and a semantic analysis allows the computer system to decipher its meaning. 

This is how computers learn to understand natural language. But that’s not to say it’s easy. It took us, humans, thousands of years to develop our linguistic systems. We communicate through language without having to think too much about it because we’ve been programming our brains on how to use language since we were children. Our brains have also adapted to learn over generations during our evolution as a species. Communicating with each other through language involves a sophisticated multi-sensory effort, and the language centers in our brains are constantly working. 

That’s why teaching computer systems how to understand and use human language is such a difficult task. Words can change meaning depending on the context, and they can be combined in infinite ways. 

Oftentimes, the meaning of the information conveyed also depends on the cultural context, adding another layer of ambiguity for computer systems to navigate. Computer systems typically need people to communicate with them through an unambiguous and highly organized programming language.

The fact that language — and how people use it — is constantly changing further complicates the process. Language has rules, but they are not set in stone and can evolve through time. If real-world language changes over time, hard computational rules that work now may become out of date.

The use of abstract language is notoriously difficult for computers to interpret. Sarcasm, for example, is difficult to detect with NLP techniques. Another example is that the meaning of a sentence might alter depending on whatever word or syllable the speaker emphasizes.

When performing speech recognition, NLP algorithms may overlook small yet essential tone changes in a person’s voice. Additionally, the tone and inflection of speech can change depending on a person’s accent, making it problematic for the computer system to parse.

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