Hello! Today I will be talking about one of the most hyped topics of artificial intelligence and my personal favorite Natural Language Processing, in short, NLP. Before going through what is natural language processing and how it works, let’s see how the concept originated in the first place.
A Brief History of Natural Language Processing
Let’s go back to 1950 to find out the history of NLP: 1950 – In 1950, Alan Turing proposed the well-known ‘Turing Test’ in his paper, ‘Computing Machinery and Intelligence’, where he introduces a modified version of an ‘Imitation Game’. In this test, human judge ‘C’ should determine who among A (a machine) and B (a human) is a human, over interrogations. Any machine which would be able to fool the interrogator would pass the test. Turing argues that any machine with sufficient physical resources could be programmed to give answers as close as a human can give. This is believed to be the foundation of NLP that we see today. Soon after this, several attempts were made in the field of NLP – the structural transformation of natural language into a machine-readable format, complex rule-based systems were created to make computers understand the natural language. Since these were not satisfactorily good enough, NLP and AI seemed to lose their charm. 1960 – Chomsky and others shared their work on formal language theory and generative syntax. 1980 – By the 1980s, with the scope of machine learning increasing, NLP started shifting towards machine learning too. In 1985 – Terry Sejnowski created a neural system that could learn how to pronounce English words. 1990 – Probabilistic and data-driven models had become quite standard by then. 2000 – Engineers had a large amount of spoken and textual data available for creating systems Today, a tremendous amount of work is being done in the field of NLP using Deep Neural Networks or Machine Learning in general, where we are able to create state-of-the-art models in text classification, QnA generation, Sentiment Classification, etc. Impressive, isn’t it?
What Is Natural Language Processing in Artificial Intelligence?
Natural Language Processing (NLP) is a branch of artificial intelligence that aids computers to understand, interpret, and manipulate human language. NLP originates from various disciplines, including computer science and computational linguistics, to bridge the gap between human message and computer understanding.
Natural Language Processing: Know the Basics
There are two major components of NLP:
Natural Language Understanding (NLU) In natural language understanding, we have the following tasks:
- Mapping the input given in natural language into meaningful representations
- Analyzing the various aspects of the language
- Natural Language Generation (NLG)
NLG is the procedure of generating meaningful sentences and phrases in the form of natural language for some internal representation.
Natural Language Processing: Overview
Let’s find out some of the most commonly used Natural Language Processing algorithms when defining the vocabulary of terms:
Bag of Words: This model is commonly used to count the words in a piece of text. It generates an occurrence matrix for the document/sentence, disregarding grammar, or word order.
Tokenization: Tokenization is a method of segmenting running text into sentences and words. Basically, we are cutting a text into smaller pieces called a token, while discarding characters such as punctuation marks.
Stemming: Stemming is the process of slicing either the end or the start of words to remove affixes (the lexical additions to the root of the word). Let us explain through an example. Affixes that are attached at the beginning of a word are known as prefixes (e.g., ‘Astro’ in astrobiology) and those attached at the end are suffixes (e.g. ‘ful’ in ‘helpful’).
Lemmatization: It resolves words to their dictionary form (also known as lemma). It needs detailed dictionaries for the algorithm to look and link words to their corresponding lemmas. For instance, the words ‘sleeping’, ‘sleep’, and ‘slept’ are basically forms of the term ‘sleep’. Therefore, sleep is the lemma of all of them.
Why Is NLP Important?
In simple words, Natural Language Processing involves machines learning applications to understand language that humans speak, analyze it, manipulate it, and give intended results.
Well, ‘Processing’ is a broad term. In order to understand the role of NLP, we will have to look into some scenarios.
How Natural Language Processing Works
Firstly, the most obvious point – Natural Language is the language that we speak. So if a machine can understand the language we speak, it makes the interaction between a machine and a human, much smoother.
Analyzes Huge Amount of Data Efficiently
Imagine the amount of textual and speech data we have over the internet today – A lot!!! A lot of information is always good, but it is obvious that allocating human resources for processing such an enormous amount of data isn’t really practical. NLP has certainly automated the process of analyzing and extracting relevant information out of large volumes of data.
Converts Unstructured Data into Any Required Structure
Suppose, the market strategist of a company wants to know how people have reacted to their new promotional event. People will have expressed their views over any social media or other internet platforms out there. What does the task include in general –
- Collect all the comments relevant to this promotional event from internet resources.
- Identify various levels of sentiments/reactions which could be used to classify the data.
- Classify the comments into the above various levels of sentiments/reactions.
- Find statistics for each of the reactions to get an overall view of data
- When you observe the above steps, each of the above steps needs NLP in one or the other way since we are ultimately dealing with the text.
There are 5 basic steps in NLP:
Lexical Analysis – analyze the structure of words
Syntactic Analysis (Parsing) – find out the relationship between words
Semantic Analysis – mapping of syntactic structures and objects
Discourse Integration – brings meaning to immediately succeeding sentence
Pragmatic Analysis – data is interpreted on what it actually meant
This is the standard prototype followed by all NLP-based systems to interpret human language.
What Is Natural Language Processing Used for?
Since NLP itself is a vast field, the way concepts of NLP are being used in various domains is enormous. Here I have listed out some of the most important applications of NLP, which are being used in a variety of projects today!
- Language Translation – Whether you want to understand a catchy song from an unknown language or write ceremonial speech in another language, translators are something we all would want to have. Before machine translation was a thing, human translators were all we had. Our scholars realized the necessity of machine translation pretty early and that is why translation is one of the oldest applications of NLP.Today we have Google Translate, Bing Translator, etc providing us with free translation services. Many applications allow us to translate conversations in the app, for example – Facebook.
- Question and Answering – Amazed at seeing how conversational customer support chatbots can answer you instantly when you ask them something? QnA maker and Answer Retrieval, have been two of the most trending research topics in the field of NLP today. You must have seen how Gmail suggests replies to the mails you receive – this is nothing but automatic answer generation.
- Speech to Text conversion – We all have used Siri, Google Assistant, etc. When we give voice commands to such bots, these commands are first converted into text and then processed to perform further actions.
Language Modeling – The language model is a probabilistic distribution over phrases/words. In simple words, it is the numerical representation of one or more languages where the semantics of the languages are preserved. Thanks to the Language Models, we are able to produce state-of-the-art results in various tasks of NLP - Grammar/Spelling correction – While I’m writing this blog, I can’t ignore how good the spelling and grammar corrector does Google Docs have and how overwhelmingly useful it is. Most of the NLP applications have been using spelling and grammar correction techniques which could result in better accuracy for other NLP tasks.
- Text classification – Text classification finds its usage in almost any NLP-related tasks – sentiment classification, anomaly detection, intent classification, etc.
- Document summarization – Document summarization as the title explain, refers to the generation of a shorter document without loss of any useful data. It helps primarily in indexing documents by using unbiased summarization, unlike humans.
- Sentiment Analysis – Market analysis for a retail company, analysis of social media trends, public opinions, on the ruling government are some of the examples where sentiment analysis is primarily required. Looking at the above applications of Sentiment Analysis, we can argue that while it can help brands/companies in improving customer service, planning marketing strategies, product designing,etc., it can even help politicians to plan their campaigning strategies. Haha, this is why AI is for everyone, isn’t it? 😀
Benefits of Natural Language Processing
NLP is a wide-spread domain offering several benefits. Here are some of them:
Offers Immediate Customer Service
Even if you have never heard about NLP, we bet you must have chatted with a chatbot. Chatbots use NLP to avoid customers ever waiting for a service desk response.
Moderates User-Generated Content
You can use NLP to enforce spam filters and block unwanted content on websites and emails. You can also filter out comments with hatred, or inappropriate content.
Boosts Your Conversion Rate
NLP solutions are unmatched masters of conversion optimization. Features such as auto-complete text and advanced functionality are helping businesses boost their conversion rates.
Enhances Customer Experience
Users can quickly find what they want, get instant help, access high-quality content, and much more – resulting in a next-level user experience.
I hope, after reading this blog, you have quite an idea of how versatile NLP is! Do follow our blog page for more such insights on AI and Conversational AI chatbots in customer service.
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