What is Natural Language Processing(NLP) and its different tasks

Natural Language processing

In this article, we will explore and understand what is natural language processing and how is NLP helpful in the real world. NLP is used in the world all around us. A good way to gain a deeper understanding is by exploring the problems that NLP solves and then categorizing their task in NLP. Let's start by understanding some of the fundamentals of NLP.

Natural language processing is an area of computer science and artificial intelligence. That involves the study of human language through algorithms. It's a combination of computational linguistic, machine learning, and deep learning models. The goal of NLP is to 'understand' language's whole meaning along with the complete intent and sentiment. Some popular applications are Amazon Alexa, Apple voice assistant, Siri, and tasks such as machine translation you have seen in Google Translate.

Let's explore some of the problems solved by NLP. NLP has helped us solve some real-world problem. Let's go over some of them. First, let's start by language modeling and classification. Language modeling involves the task of predicting the sequence of words to be used in a sentence based on how it was used in the past.

Real World problems solved by NLP:

This is used for tasks such as speech recognition, machine translation, etc. On the other hand, classification in NLP is used to bucket similar objects like sentence, paragraphs or documents together in tasks such as classifying spam emails or identifying different sentiments etc.

Next is information extraction and retrieval. Information extraction and retrieval involves the task of finding and retrieving relevant information. This can be broken down into two components. One is to extract information such as to the people mentioned in an email, and the second is to retrieve information based on query from collection of data.

This is done by search engines such as Google and Bing. The next is conversational agents, commonly referred to as chatbots. Based on our understanding of language, dialogue systems can be built to interact with users to understand their intent and provide them with the relevant information.

Virtual assistants such as Alexa and Google Assistant are some great examples of conversational agents. The next is machine translation. Machine translation includes the translation of text from one language to another. This is done by applications such as Google Translate for both voice as well as text, where, for instance, English can be translated to Spanish. The last is topic modeling.


When a large collection of information is present, topic modeling involves a task of figuring out the underlining topical structure where a user can understand the important topic being discussed. Google News is one great example where algorithm understands the topic of each article and clusters similar article based on similar topic. Next we will look at some of the application in NLP bucketed by difficulty.

A good way to bucket NLP tasks can be categorize them from easy to hard. Let's explore the reasoning behind the categorization of what makes NLP so interesting to use in the real world. Starting with easy, tasks such as spell check, simple keyword-based information retrieval system, and topic modeling, are the tasks that are more rule-based and hence not ambiguous.

They are relatively easy tasks in the NLP world. The next is medium where tasks such as text classification, information extraction, and closed domain chatbots are intermediately difficult, as this involves multiple rules that needs to be conditionally modified based on the scenario. For instance, in sentiment analysis, some words mean entirely different when considered as two words.

Various tasks in NLP:

Now that we have a broader idea of how NLP is used in the real world, let's understand the various tasks that NLP is used for in the real-world scenarios. The reason that NLP is such an interesting area of study is because it's one of the primary ways that humans communicate. Teaching a machine to do it would mean that machines can understand us more efficiently. NLP is already present all around us.

Let's have a look at how NLP is used in real world. We'll explore it from the perspective of fundamental task and components of NLP. Let's start with fundamental tasks. NLP assists with multiple fundamental tasks such as searching through a portion of text, machine translation from one language to another, summarization of documents, recognizing entities based on semantic context, tagging parts of speech such as noun and verbs, and even identifying relationship between parts of sentences, retrieving information and grouping of similar words.

Now let's look into components of NLP. NLP has multiple components, starting with morphological and lexical analysis. Morphological and lexical analysis deals with textual analysis at the word level. It focuses on analyzing language from morpheme level. Second is syntactic and semantic analysis.

Language is also analyzed based on grammatical rules known as syntactic analysis, and based on the underlining meaning based on context known as semantic analysis. Third is discourse integration. Discourse integration is the sense of the context. The meaning of any single sentence depends on the sentence that were before it and helps form the context of the sentence that follows it.

For instance, in the sentence: she wanted it, it would depend on the prior discourse context. And the last is pragmatic analysis. Pragmatic analysis is about the overall communication, social content and its effect on the interpretation. In this analysis, the focus is on how it was interpreted rather than what is said. In the following articles, we learn about the different approaches to NLP.

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