Practical examples of Natural Language Processing

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Practical examples of Natural Language Processing

If you have just learned about Natural Language Processing(NLP) or are thinking about why it is useful, you are the right place. The emergence of NLP came with the Turing test in the 1950s as an unprecedented criterion of artificial intelligence and is considered as an amalgamation of artificial intelligence, linguistics and computer science. Understanding natural language is the focal point of NLP and the center of many revolutionary technologies i.e. translation and question answering. NLP has been a challenge for computer scientists and has numerous interdisciplinary applications. While there are many important subtasks of NLP that contribute to solving bigger and more mainstream tasks, in this article we will talk about conventional ideas with some practical examples. For better understanding, we divide our NLP fields into four main categories namely Syntax, Semantics, Speech and Discourse.

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Syntax

Syntax in natural language helps us with the rules of the language. It tells us how the words are arranged, how clauses are marked, sentence correctness, part of speech and in general the knowledge of grammar in the language. Every language operates differently and cannot be understood without the syntactic understanding of that language. Syntactic analyses are the core of NLP and have many practical examples such as word writing software i.e. Grammarly, Microsoft Word etc. Even word predictors in your smartphones use syntactic rules of NLP for the next possible prediction.

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Semantics

The semantics of the language takes us one step ahead and helps us with understanding the context of the sentence, paragraph or a document. It is done by first identifying the meaning of the text and then assigning them the grammatical role. This helps in understanding the context of the word, the sense of the paragraph and can help us in tasks like topic understanding or emotion evaluation. Semantics is everywhere around you, Google, Alexa, Siri, Cortana, no modern NLP application can do without the semantic understanding of the text. Some really important topics that you would like to learn about in semantics should be Named Entity Relations(NER), Question Answering(QA), Sentiment Analysis, Relationship Extraction, Natural Language Understanding(NLU) and Natural Language Generation(NLG) etc.

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Speech

Speech gives NLP an entirely different dimension where you can use your voice commands to interact with the computer. Automatic Speech Recognition(ASR) is implemented in all the leading AI assistants, translating the recognized text and parsing it for further operations. The textual information from the query or command is later returned as a verbal or written response. Speech has challenges like accent recognition, noise, misinterpretation of text and thus the context of the previous conversation also plays a huge role. What seems very direct to us, has a plethora of researches combined to deliver what can be considered as human interaction friendly. If you like what you read, give topics like hybrid-dialogues, task-oriented dialogues, dialogue act recognition some look.

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Discourse

Discourse in a modern and multi-layered NLP field which unfolds linguistic structures and features from texts with multiple coherent sentences. But why is it important to understand discourse? Computers need to know discourse in the text to reach a human-level understanding. Without discourse tasks like Machine translation, Information Extraction, Sentiment analysis, Text summarization will not reach the heights that they currently stand on. If you have ever made a summary of text by reading the whole article, in the computer world, that can be done now. All thanks to discourse.

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Some more examples

If that was boring for you, let’s add some more fun. Plagiarism checker is a very popular example of NLP tasks and if you are a student, you would know about software like Turnitin. Our emails since decades have been using NLP techniques to filter spams, hate speech, promotions etc. We also see the business world utilizing NLP to reach new levels. NLP integrated with data analyses has taken the world by storm and every other business wants to adopt artificial intelligence to increase revenue, reach a more targeted audience, bring automation to redundant tasks and achieve a better understanding of the customers. IBM Watson, Google Cloud services and Amazon comprehend are just a few examples to name.

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