What is Natural Language Processing (NLP) and its real-life uses?
Have you ever wondered how does the Google suggestion system work? Or how does Google or Siri, or Alexa respond to your questions? Or even how chatbots work in specific applications and web pages? These questions motivate the need to study the importance of textual data and how we can work with it. We use Natural Language Processing, NLP, to solve all the above problems. In this article, we will first introduce what NLP is, some of the tasks we solve using NLP, some of the popular toolkits in use, and their real-life applications.
What is Natural Language Processing (NLP)?
Natural Language Processing, or NLP, is the field of Machine learning or Artificial Intelligence responsible for making computers understand wordy data, either in text or speech, just like we do. We already have Machine Learning methods for implementing regression and classification tasks. We use neural networks and Computer Vision to deal with image-oriented tasks. But, when it comes to textual data, something other than these methods will work. Because as you can notice, there is a dimension of time associated with the text. Computers need to analyze words in a sentence or speech by using information surrounding that word in the data. And none of the techniques mentioned above help us do that. Therefore, this is where NLP allows us to solve these specific problems.
NLP combines “computational linguistics” with “Machine Learning models” to help us understand a language. Computational Linguistics is a rule-based way of looking at language. We define specific rules to which a language adheres. Then, we use these rules to develop a basis for the statistical models to check and build. This form of representation of human language enables computers to understand and analyze.
What are the different NLP tasks?
Human language is highly complex. It contains rules, prepositions, conjunctions, grammatical structures, and more. Humans have nearly perfected our usage through years of working through it. But computers and, more specifically, NLP models do not have that luxury of time. Therefore we break down the entire textual data problem into specific subtasks which will use NLP models. We localize our problems and pay more attention to innovative solutions rather than wasting time on fixing rules for language.
Let’s look at a few of the NLP tasks below which we commonly face in our day-to-day lives:
- Speech Recognition: As the title suggests, speech recognition or speech-to-text is a method to convert voiced text into written text. The model receives audio input from the user. Using this input, the model breaks down the speech into individual words and tries to make sense of them. The biggest challenge is implementing speech recognition in real-time because of people’s talking speed, accent, diction, and so much more.
- POS tagging: POS tagging or Part-of-speech tagging provides every word in the sentence with a definite “tag.” A word can be a preposition, conjunction, adverb, verb, noun, etc. We use POS tagging to classify the words in a sentence into their respective categories. This task is challenging because it needs temporal data to determine a word’s tag correctly.
- Word sense disambiguation: Word sense disambiguation is the task we use to find the correct sense of a word among multiple possible senses based on semantic analysis of the sentence. The selected sense of that word best fits the context of the sentence.
- Named entity recognition: Named Entity Recognition is used to identify named entities or “Proper Nouns” in the text. We assign the tag of the named entity to these phrases, which are helpful later on in the text.
- Sentiment Analysis: As the name suggests, we use Sentiment Analysis to judge the sentiment or the feelings behind a comment or text provided to the model. We use sentiment analysis to extract human emotions from the text, such as happiness, sadness, sarcasm, anger, etc.
What are the different NLP toolkits?
As we know, NLP toolkits are libraries that contain all the essential functions for word segmentation, sentence parsing, POS tagging, and so on. Therefore, I will list down a few NLP toolkits from which you can build your base:
- NLTK: The most popular Python NLP library.
- Stanford Core NLP
- SpaCy: This is used specifically for advanced NLP tasks
Real-life uses of NLP
Let’s go over a few real-life uses of Natural Language Processing:
As we know, people receive so many spam emails and phishing threats. Scientists and even the email industry uses NLP to detect if the sent mail has wrong grammar, unnecessary urgency, and threatening message to detect if the mail is spam mail or not.
People traveling abroad often require help with language translations. This is the concept of Machine Translation. Machine Translation requires extensive NLP as we need to follow the rules of one language and break down sentences into words. Then, the model must understand the semantics of the sentence. We follow these steps by converting this semantic into another language with different rules and words. This is a continually growing area for research with enormous scope in the NLP domain.
Virtual Assistants and Chatbots
Virtual assistants such as Siri, Alexa, and Google Assistant have taken the speech recognition world by storm. They now understand multiple languages and almost always correctly realize what we have to say. These assistants follow the NLP task of speech recognition. They receive audio data from the user and use NLP models to break it down and make sense of it. Similarly, chatbots use the same idea but in the textual meaning. They communicate with the user over text messages and try to understand the semantics of the user’s message.
Social media is a place where everyone can voice their opinions. However, there is a large number of moderators looking over the messages sent by people. Their job is to ensure that people’s statements do not hurt other people’s sentiments. But still, it is not humanly possible to monitor the millions of messages sent daily. Therefore, we employ NLP models to perform these tasks. These models have specialized in learning the language of social media with all the abbreviations and internet slang and detect harmful messages with very high accuracy.
Summary of Texts
We also use NLP models to understand large chunks of text and create effective summaries. This requires models that can look at a very long temporal frame to join even those sentences far off in the text. It also uses the concept of Natural Language Generation.
With this, we conclude our introduction to Natural Language Processing. But, NLP is not just about learning models and implementing them on subtasks. There is a lot of theory behind every task and how to proceed. We apply essential tools in nearly every NLP application, and we must know before creating models. You need to be theoretically sufficient and robust in implementation. This article is just a stepping stone for you to appreciate the need for NLP and study further. Happy Coding!
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