Why natural language processing is important

  • NLP is an interdisciplinary subfield of computer science and information retrieval, encompassing a range of techniques and methodologies for analysing, understanding, and generating text and speech.
  • Several key aspects of NLP enable computers to understand, interpret, and generate human language effectively, such as text analysis, semantic analysis and sentiment analysis.
  • NLP is also an important field of AI that focuses on the interaction between computers and humans through natural language in a way that is both meaningful and useful, making AI-related tools more helpful and effective.

Natural language processing is a combination of computational linguistics, rule-based modeling of human language, with statistical, machine learning, and deep learning models, bridging the gap between human language and computer understanding, making technology more intuitive, efficient, and capable of handling complex language-related tasks.

What is natural language processing?

Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches.

NLP encompasses a range of techniques and methodologies for analysing, understanding, and generating text and speech. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.

NLP has its roots in the 1940s when Alan Turing published an article titled “Computing Machinery and Intelligence” which proposed what is now called the Turing test as a criterion of intelligence, though at the time that was not articulated as a problem separate from AI.

Turing and the ‘Manchester Baby’ computer he helped create

NPL enables the development of applications that can understand and respond to human language in a way that is increasingly accurate and contextually appropriate.  The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorise and organise the documents themselves.

Key aspects of NLP in computing

NLP encompasses several key aspects that enable computers to understand, interpret, and generate human language effectively. Text analysis involves breaking down text into its fundamental components. Techniques such as tokenisation, which divides text into words or phrases, part-of-speech tagging, which identifies grammatical elements like nouns and verbs, and syntactic parsing, which analyses the grammatical structure of sentences, are central to text analysis.

Also read: What is text data mining?

Semantic analysis focuses on understanding the meaning behind words and sentences. Tasks like word sense disambiguation help determine the correct meaning of a word based on its context, while named entity recognition identifies and classifies proper nouns, such as names of people, organisations, or locations. Sentiment analysis evaluates the emotional tone of a piece of text. It classifies text as positive, negative, or neutral, making it useful for applications such as social media monitoring, customer feedback analysis, and market research.

Information retrieval involves finding relevant information within large datasets. It includes tasks such as searching for specific documents, retrieving relevant data from databases, and answering user queries based on a repository of information. Text generation involves generating coherent and contextually relevant text based on input data. Techniques such as text summarisation, which condenses long documents into shorter summaries, and language modelling, which predicts the next word in a sentence, are key to text generation.

NLP’s applications in AI industry

NLP is crucial for various fields, transforming the way we interact with technology and providing numerous practicals. It is also a field of AI that focuses on the interaction between computers and humans through natural language.

Chatbots and virtual assistants

NLP is fundamental in developing chatbots and virtual assistants like Siri, Alexa of Amazon, Google Assistant, and Cortana of Microsoft. These AI-driven systems understand and respond to user queries in natural language, providing information, performing tasks, and engaging in conversations. For example, chatbots on customer service websites can answer common questions, resolve issues, and guide users through processes without human intervention.

Machine translation

NLP powers machine translation systems like Google Translate, which can translate text and speech from one language to another. These systems use advanced algorithms and neural networks to provide accurate translations, facilitating communication across different languages. This is particularly useful for travellers, international businesses, and multilingual communities.

Speech recognition and voice assistants

Speech recognition technology, an essential aspect of NLP, allows AI systems to convert spoken language into text. This capability is used in applications like transcription services, voice-controlled devices, and virtual assistants. For example, Dragon NaturallySpeaking enables users to dictate text and control their computers using voice commands, which is especially beneficial for individuals with disabilities.

Also read: What is a conversational AI platform?

Document summarisation

NLP is used to automatically summarise long documents, extracting key points and condensing the information into a shorter form. This is valuable for quickly understanding large volumes of text, such as research papers, news articles, and legal documents. Tools like Microsoft’s Word Editor and SummarizeBot use NLP to provide concise summaries.

Named entity recognition (NER)

NER systems identify and classify entities in text, such as names of people, organisations, locations, dates, and more. This information is crucial for applications like information retrieval, knowledge management, and content categorisation. For example, NER can be used to extract important details from financial reports or legal contracts.

Security and fraud detection

NLP aids in identifying fraudulent activities by analysing communication patterns and detecting anomalies. Financial institutions use NLP to monitor transactions and flag suspicious behaviour that might indicate fraud or money laundering. For example, NLP algorithms can analyse emails and messages to detect phishing attempts and prevent cyberattacks by identifying patterns commonly associated with malicious intent.


Monica Chen

Monica Chen is an intern reporter at BTW Media covering tech-trends and IT infrastructure. She graduated from Shanghai International Studies University with a Master’s degree in Journalism and Communication. Send tips to m.chen@btw.media

Related Posts

Leave a Reply

Your email address will not be published. Required fields are marked *