From the dawn of humanity to the present day, knowledge has steadily increased as we know it today. As a result of centuries of research, often on seemingly unrelated topics, we have acquired scientific knowledge, which is the most important resource available to humankind. From the combined efforts of computer science and applied linguistics emerged Natural Language Processing (NLP), whose objective is none other than to enable the computer-assisted comprehension and processing of information expressed in human language—in other words, to make communication between people and machines possible.
There are different programs that exhibit varying degrees of intelligent language processing. For example, a document search engine might simply search for documents containing a string of characters specified by the user, regardless of whether that string has any meaning in a language. In this case, it wouldn’t be an application of Natural Language Processing (NLP). However, the same search engine could search for documents that convey the idea specified by the user, regardless of the characters used. In this case, it would undoubtedly be an excellent application of NLP, as it would understand the idea in the user’s request and in each document and be able to compare them.
Architecture of a Natural Language Processing (NLP) System
The architecture of an NLP system is based on a levelled definition of natural language, which is as follows:
- Phonological Level: It deals with how words relate to the sounds they represent
- Morphological Level: It deals with how words are built from smaller units of meaning called morphemes
- Syntactic Level: This deals with how words join to form sentences, establishing each word’s structural role and which phrases are part of other phrases.
- Semantic Level: This deals with the meaning of words and how meanings come together to give meaning to a sentence; it also refers to meaning independent of context, that is, of the isolated sentence.
- Pragmatic Level: It deals with how sentences are used in different situations and how usage affects the meaning of sentences.
How Does an NLP System Work?
This architecture shows how the computer interprets and analyzes the sentences provided to it:
- The user tells the computer what they want to do.
- The computer analyses the provided sentences, in the morphological and syntactic sense, that is, whether the phrases contain words composed of morphemes and whether the sentence structure is correct.
- The next step is to analyze the sentences semantically, that is, to know what the meaning of each sentence is, and to assign the meaning of these to logical expressions (true or false).
- Once the previous step is completed, we can now perform a pragmatic analysis of the instruction. This means that, after analyzing the individual sentences, we now analyze them all together, taking into account the context of each sentence. Once this step is completed, the computer knows what it will do; in other words, it already has the final expression.
- Once the final expression has been obtained, the next step is to execute it in order to obtain the result and be able to provide it to the user.
Applications of Natural Language Processing (NLP)
Having seen how natural language processing develops, we can therefore deduce some of its many applications, among which are the following:
| Application Area | Examples of NLP Applications |
| Personal and Professional | Time management, stress management, decision-making, personal assistants |
| Healthcare | Medical document analysis, patient feedback analysis, mental health support, clinical text processing |
| Education | Language learning, automated grading, personalized learning, educational chatbots |
| Customer Service | Chatbots, automated customer support, answering customer queries, sentiment analysis |
| Business and Marketing | Customer review analysis, market research, social media monitoring, customer feedback analysis |
| Search Engines | Semantic search, search query understanding, relevant document retrieval |
| Translation and Language | Machine translation, grammar checking, text summarization, language detection |
| Voice and Speech | Speech recognition, voice assistants, speech-to-text conversion, voice analysis and synthesis |
| Information Processing | Information extraction, document classification, named entity recognition, question answering |
| Cybersecurity | Spam detection, phishing email identification, suspicious message analysis |
| Content Creation | Text generation, writing assistance, content summarization, proofreading |
| Finance and Banking | Financial document analysis, customer query processing, fraud-related text analysis |
Other Specific Applications of NLP
Some more specific applications of NLP are voice analysis and synthesis, machine translation, linguistic review, information retrieval, question answering, information extraction, and speech recognition, among others.
Importance of Natural Language Processing in Everyday Life
As you can see, NLP is present in our daily lives much more than we realise, which is why in recent years it has become so crucial for different areas in which we operate, making it an indispensable tool for companies when it comes to obtaining information and using it to their advantage.
Also Read: The Role Of Chatbots In The Customer Journey And How To Improve