Natural Language Processing

Discover how Natural Language Processing (NLP) technology is changing the way we communicate and retrieve information.

The evolution of Natural Language Processing and its impact on our daily lives

Language as our means of communication is present in every aspect of life in various forms. Human brain is capable of interpreting language input instantly and create relevant information or act accordingly in response. Natural Language Processing (NLP) is based on the idea of automation of human language analysis to perform various resource intensive tasks for humans.

The advancements in NLP technology and its impact on society

In today’s world, NLP technology is all around us in everyday life. From automatic translation on our social media posts, response suggestions on our emails to home assistant devices recognizing our voice and following our commands are all profiting from NLP technology or chatbots on websites answering customers’ FAQs. In its early stages, language processing relied primarily on rule-based systems, in which linguistics or domain experts defined the rules for interpreting language. As an example imagine in an airport information help desk website as soon as the algorithm finds the word “delay” it will forward the message to the corresponding department. Rule based systems are quite rigid and limited albeit robust.

Like many other technological fields, NLP has evolved with the rise of AI and machine learning algorithms. As well with the advancement in using parallel computational power and emergence of deep learning technology.

NLP applications: from text classification to image captioning

Some generic NLP tasks which have the lead in NLP technology are Text Classification, Machine translation, Summarization, Information Retrieval and Natural Language Generation.

Text classification is a supervised machine learning task in NLP in which a label is assigned to a piece of text. Text classification can range from general tasks such as Sentiment analysis, Spam detection to domain specific tasks such as legal document classification or Medical Diagnosis based on patient record archives.

Machine translation is the automated task of translating a natural language to another. Well known examples of such applications implementing Machine Translation are DeepL and google translator.

Automatic Summarization is a NLP task which aims at providing a summary of long documents. Two methods of automatic summarization are extractive and abstractive. Extractive summarization extracts salient parts of texts which contain the main points of multiple or one documents verbatim. One known example of extractive summarization is the text extracts provided by search engines on the searched topic. On the other hand, the goal of abstractive summarization is to interpret the input document(s) and provide a paraphrase of the main points.

Information Retrieval (IR) involves retrieving relevant information from a pool of available data based on a query. The query may contain contextual or meta data, and querying a search engine is the most prominent example of IR. NLP technology empowers search engines and has improved the quality of search results, resulting in more semantically related outcomes than relying solely on exact keywords.

Natural Language Generation (NLG) refers to the use of NLP technology to produce human language. NLG is employed in many NLP tasks such as abstractive summarization, Image captioning, question answering, machine translation and so on.

NLP Beyond Text: Multi-modal Sentiment Analysis and Other Developments

The tasks described above mainly rely on text as input and produce textual information as output. Additionally, NLP tasks such as Speech recognition, Speech Synthesis, Text to Speech, and Image Captioning are also included. Researchers have also explored multi-modal settings for tasks such as Sentiment Analysis, which involves extracting emotions from inputs that include not only language but also images and videos.

NLP technology has hugely improved over decades and had a leap forward by the invention of transformers based on deep learning technology and is now omnipresent in our modern daily life.


Natural Language Processing technology has evolved greatly since early rule-based systems. With the advent of artificial intelligence and machine learning, NLP has become a powerful tool for automating language analysis and performing a wide range of tasks. From text classification to machine translation, summarization, information retrieval, and natural language generation, NLP is changing the way we communicate and access information. As NLP continues to advance, we can expect even more exciting developments and new applications in the future.

NLP Glossar

Annotation is the task of adding layers of information to raw data. Text annotation can vary from assigning different levels of information onto text segments. Annotation can be applied on different levels of text for instance character-level, token (word)-level, clause-level, sentence level or even document level.
Information might range from basic syntactic information such as Part of speech tags (Verb, Noun,) on tokens to semantic levels such as assigning tags such as. Annotation tasks are conducted to enrich raw data and create Labeled datasets.

Supervised machine learning algorithms

Supervised machine learning algorithms are the type of algorithm which learn from labeled data. For instance, by observing a human labeled dataset of texts classified as spam or non-spam the algorithm learns who to distinguish spam emails from non-spam ones.


Training is the process in which a machine learning algorithm observes data to be able to learn from it. The output of the training process is a machine learning Model which can ideally predict the patterns it has learnt.

Language models

Language models are models which given a sequence or words can either output the probability of it belonging to a language or produce the most probable sequence occurring after a given sequence of words. Language models have been trained on huge amounts of textual data.


Embeddings in the context of NLP are numerical representations of language. Embeddings are vectors of real numbers (usually between zero and one) which include contextual information of words, phrases, sentences, and text segments. Neural networks which are the basis of deep learning technology employ embeddings in their architecture to represent natural languages.

Neural Networks

Neural Networks are a machine learning algorithm which are inspired by how human brains work using neurons to transmit data and process information. Applying neural networks in artificial intelligence has increased drastically with the employment of graphical processing units (GPUs). Neural Networks can be composed of one or more layers of neurons. Neural networks with more layers are called Deep neural networks.

Deep learning

Deep learning refers to machine learning algorithms based on deep neural networks.

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Curious about the world of Natural Language Processing and AI? From text classification to speech recognition, discover the answers to all your burning questions about NLP and its impact on our daily lives. Join us as we explore the latest advancements in NLP technology and its exciting future possibilities.