Natural Language Processing
Natural Language Processing
Natural Language Processing (NLP) refers to the branch of computer science, and more specifically to the branch of artificial intelligence or AI, which wishes to give computers the ability to understand the text and spoken words of the same. way than humans. NLP combines rule-based human language modelling of computational linguistics with statistical, machine learning and deep learning models. Together, these technologies allow computers to process human language as text or voice data and "understand" its full meaning, with the intention and feeling of the speaker or writer. computer programs that translate text from one language to another, respond to voice commands, and quickly summarize large volumes of text, even in real time. Chances are you've interacted with NLP in the form of voice-activated GPS systems, digital assistants, voice dictation software, customer service chatbots, and other consumer amenities. But NLP is also playing a growing role in our business solutions that help streamline business operations, increase employee productivity, and streamline critical business processes.
How Businesses Are Using NLP
The amount and availability of unstructured data is growing exponentially, revealing its value in processing, analysis, and decision-making potential in all businesses. NLP is a perfect tool for getting close to the volumes of valuable data stored in tweets, blogs, images, videos and social media profiles. So basically any business that can see the value of data analysis - from a short text to multiple documents that need to be summarized - will find NLP useful. Advanced systems often include both NLP and machine learning algorithms, which increases the number of tasks these AI systems can perform. In this case, they unlock human language by tagging it, analyzing it, performing specific actions based on the results, etc. Think of Siri or Alexa, for example. They are AI-powered assistants who interpret human language with NLP algorithms and speech recognition and then react based on the previous experience they have received through ML algorithms.
Natural Language Processing associated with following areas
Search: NLP algorithms can identify specific elements in text. You can search for keywords in a document, perform a contextual synonym search, find misspelled words or similar entries, and more.
Parts-of-Speech tagging ( POS): it groups the words of the text according to the parts of the speech according to the definition of the word and the context. POS tagging methods and models include lexical, rule-based, probabilistic methods as well as the use of recurrent neural networks and more.
Information retrieval: With the help of NLP, we can find the part needed among unstructured data An information retrieval system indexes a collection of documents, parses the user's query, then compares the description of each document with the query and presents its results.
Grouping of information: Grouping or classification of text is done through text tags. The NLP model is trained to classify documents based on specific attributes: subject, document type, time, author, language, etc. Text classification generally requires labeled data. Information aggregation is used for supervised machine learning, which therefore triggers a multitude of use cases.
Sentiment analysis: This is a type of text classification in which NLP algorithms determine the positive, negative or neutral connotation of the text. Use cases include analysis of customer feedback. , follow trends, conduct market research, etc. via an analysis of tweets, posts, reviews and other reactions. Sentiment analysis can encompass everything from the release of a new game on the App Store to political speeches and regulatory changes.
Machine translation: This usually involves translating one natural language into another, preserving its meaning and, therefore, producing fluent text. Different methods and approaches are used here: rule-based, statistical and neural machine translation.
Summary. NLP algorithms can be used to create an abbreviated version of an article, document, number of entries, etc., with main points and key ideas. There are two general approaches: abstract synthesis and extractive synthesis. In the first case, the NLP model creates a whole new summary in terms of phrases and sentences used in the analyzed text.
Named-Entity Recognition(NER) : is an extraction, identification and categorization of entities. This involves extracting the names of places, people and things from the text and classifying them into certain categories: person, company, time, place, etc. Use cases can include classifying content for SEO, customer support, analysis of patient lab reports, academic research, and more.
Answer the questions. An automated question response system applies a range of NLP techniques to analyze unstructured documents, from Wikipedia articles to social media news feeds or medical records, retrieving the necessary information, analyzing it and using it. the best part to answer the question.
Automatic Speech Recognition (ASR): NLP techniques are actually designed for text, but can also be applied to voice typing. The ASR transcribes the oral data into a word stream. Neural networks and hidden Markov models are used to reduce the speech recognition error rate, however, it is still far from perfect. The main challenge is the lack of segmentation in oral documents.And while human listeners can easily segment spoken input, automatic speech recognition provides annotated output.
| Natural Language Processing
Natural language processing can add value to any business that wants to leverage unstructured data. Applications triggered by NLP models include sentiment analysis, summary, machine translation, query response, and more. Although NLP is not yet independent enough to provide human-like experiences, solutions using human-applied NLP and ML techniques dramatically improve business processes and decision-making.