Natural Language Processing
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.