The Basics of Natural Language Processing

What exactly is Natural Language Processing

I will start by saying that natural language processing also called NLP is just basically you talking to your computer in a language most comfortable to you, and then the computer does all the heavy lifting of trying to understand you.

NLP is actually a branch of computer science, and this is because the importance of human-computer interaction cannot be over-emphasized. For AI to reach its full potential then it must be able to understand humans in the most basic and natural form, hence the field of study.

The Input and output of NLP applications can be in two forms:

- Speech

- Text

NLP is a field that merges rule-based modeling of human language with statistical, machine learning, and deep learning models, thus enabling computers to understand human language in both text and speech form. This helps computers interpret the meaning of language precisely, including the writer or speaker's the intention and emotional state. let’s look closely at text-based NLP.

TEXT PREPROCESSING

So, NLP is the attempt to make your computer understand you better, text preprocessing then is the first step it takes to translate what you say to it into something it would better understand. It basically involves transforming text into a clean and consistent format that can then be fed into a model for further analysis and learning.

The text is taken to various stages of processing for it to be properly understood by the computer, some of these stages are;

· Segmentation

· Normalization

· Spell correction

· Stop-words removal

· Change case

· Tokenization

These is to name few; these stages are known as the NLP preprocessing pipeline. It is not placed in any specific order as it may vary depending on the application. The output of one stage often serves as the input of the next.

TEXT REPRESENTATION

In a bid to better understand you, whatsoever you say to the computer has to eventually get interpreted in a language the computer understands, and computers just happen to understand numbers way better than we do, this makes it important for all the words that have been given to the computers in language natural to us to be translated into a language that is more natural to the computer.

Text representations can be broadly classified into two sections:

Discrete text representations

Distributed/Continuous text representations

We are not going to look deeply into any of this for now as we are only introducing the concept.

TEXT CLASSIFICATION

Now the computer has gotten to a point where it can understand what you are saying to it, to better serve it would have to do what it does best, analyze and organize what it has been given.

Text classification, which can also be referred to as text tagging or text categorization, is the procedure of organizing text into predefined groups. By utilizing Natural Language Processing (NLP), text classifiers can automatically analyze text, and then assign tags or categories based on its content.

NAMED ENTITY RECOGNITION

The input has been received, structured, represented in a language that the computer would understand better, and also categorized. The computer then tries to identify the named categories from its previous process. At this point, it has dissected the inputted natural language and is attempting to better recognize statements, and phrases that make up the message that has it received. This is a very important task of NLP where the computer really tries to derive meaning or map words and phrases to actual entities such as things, people, organizations, monetary values, etc.

There is also a Named entity representation process, named entity representation, on the other hand, is about converting these Named Entities into a machine-tractable representation. This involves associating each named entity with a type and a unique identifier, which can be used for further processing. The most common way to represent named entities is by using XML tags, such as <Person>, <Location>, <Organization>, etc. Additionally, each named entity can be assigned a unique identifier, for example, a Wikipedia page ID or a DBpedia URI.

NLP COULD TELL HOW YOU FEEL THROUGH SENTIMENT ANALYSIS

We have seen the journey an NLP system takes us through to make our interactions with the computer so good, it even gets better, sentiment analysis is a natural language processing technique that could determine whether data received is either negative, positive, or neutral.

Sentiment Analysis is a branch of Natural Language Processing (NLP) that works on extracting and processing subjective information and transforming it into consumable insights. The goal of sentiment analysis is to understand the attitude, opinions, and emotions of people toward certain topics or ideas. Sentiment analysis can be performed on various forms of text, such as social media posts, reviews, news articles, and more. Its applications range from brand monitoring, social listening, and customer service to market research, product analysis, and more.

This kind of analysis is mostly done on text-based data, its use case is found majorly in the world of businesses, to monitor brand product sentiment in customer feedback and understand customer needs

LANGUAGE MODELING OR MIND READING

An NLP system via the use of language models over time after it has learned a language, which basically means after it has been able to analyze and crunch enough data, creates a probabilistic statistical model that determines the probability of a given sequence of words occurring in a sentence based on the previous words.

This basically is what makes it an AI, it cannot only understand and interpret the natural language, it is now able to predict the direction of supposed conversation based on its previous entries, isn’t that great? It uses the last one word (unigram), last two words (bigram), last three words (trigram), or last n words (n-gram) to predict the next word as per our requirements.

Language models are a crucial component of Natural Language Processing as they help in converting qualitative information about text to quantitative information. This quantitative information is then used by machines to understand natural language. Various industries such as tech, finance, healthcare, military, etc. use language models in their applications. We encounter language models daily, whether it's the predictive text input on our mobile phones or a simple Google search. Thus, language models are an essential part of any Natural Language Processing application.

MACHINE TRANSLATION IS THE ART OF SPEAKING ALL LANGUAGES

With all the big advantages we get from NLPs here is one that we literally can’t separate from our lives. Machine translation, is translating one natural language to another, a source language to a target language.

Using corpus methods, more complicated translations can be conducted, taking into account better treatment of contrasts in phonetic typology, express acknowledgment, and translations of idioms, just as the seclusion of oddities. Currently, some systems are not able to perform just like a human translator, but in the coming future, it will also be possible.

There are different types of machine translation

-Statistical Machine Translation

-Rule-based Machine Translation

-Hybrid Machine Translation

-Neutral Machine Translation

CHATBOTS AND CONVERSATIONAL AGENTS

So, the computer can listen to what you have to say, interpret it, understand it, detect the emotions likely being portrayed in the conversation, and even predict the next couple of words you may have to say, in my opinion, it is perfectly capable of having a conversation with you. Chatbots is the younger brother that only understands a small range of things and can respond to them, use case of such would be on websites where chatbots are integrated, you can go there and ask it some questions and make inquiries and it can respond to it, however, if it is unable to process the question it transfers you to a live agent who picks up from where it stops. A conversational agent on the other hand can handle more complex conversations and respond to them. Conversational agents are gradually acting in the capacity of customer service representatives as they can have perfect conversations with people with a human touch.

THE FUTURE OF NATURAL LANGUAGE PROCESSING

I believe that NLP has an obvious trajectory, it is being developed to the point that it can understand and have a general understanding of human language. Basically, the aim is for us to be able to have a conversation with our computers without having the feeling we are communicating with a lifeless object. Also, it is the aim of the developers of the technology to ensure that value ease is gotten from conversations on our devices. We just may be looking at a world where people can communicate their feelings to the computer and know that they are understood. We are not there yet but we definitely are on the path that would lead us to that future we all desire.