Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. Speech recognition has many applications, such as home automation, mobile telephony, virtual assistance, hands-free computing, video games, and so on.
Text to Speech :
This is the application of Speech recognition where the machine converts text into speech so that it could be easily listened.
Ex: Speechify is a startup that focuses on creating Audiobooks from any text.
Machine Translation (MT) is the task of automatically converting one natural language into another, preserving the meaning of the input text, and producing fluent text in the output language. While machine translation is one of the oldest subfields of artificial intelligence research, the recent shift towards large-scale empirical techniques has led to very significant improvements in translation quality.
Language Generation and Summarization
Text summarization refers to the technique of shortening long pieces of text. The intention is to create a coherent and fluent summary having only the main points outlined in the document. This is category under Natural Language Generations which uses techniques to produce written or spoken narrative form a dataset. NLG is related to computational linguistics, natural language processing (NLP) and natural language understanding (NLU).
Question Answering (QA) system is an information retrieval system in which a direct answer is expected in response to a submitted query, rather than a set of references that may contain the answers. The basic idea of QA systems in Natural Language Processing (NLP) is to provide correct answers to the questions for the learners.
Stanford’s SQUAD is a popular dataset for practicing Question Answering. Check the leaderboard here.
Named Entity Recognition (NER)
Named entity recognition (NER) — sometimes referred to as entity chunking, extraction, or identification — is the task of identifying and categorizing key information (entities) in text. An entity can be any word or series of words that consistently refers to the same thing. Every detected entity is classified into a predetermined category. For example, an NER machine learning (ML) model might detect the word “Apple.com” in a text and classify it as a “Company”.
Spell Checking & Auto Suggestions
Spell checker (or spell check) is a software feature that checks for misspellings in a text. Spell-checking features are often embedded in software or services, such as a Chatting apps, word processor, email client, electronic dictionary, or search engine.
A basic spell checker carries out the following processes:
- It scans the text and extracts the words contained in it.
- It then compares each word with a known list of correctly spelled words (i.e. a dictionary). This might contain just a list of words, or it might also contain additional information, such as hyphenation points or lexical and grammatical attributes.
NLP In Businesses
Hiring and Recruitment
NLP helps is searching and filtering the candidates through 1000s of resumes, that meet the job requirements.
Natural language processing helps in the identification of new audiences that are potentially interested in certain products. Natural Language Processing is a great source for intelligent targeting and placement of advertisements in the right place at the right time and for the right audience.
With NLP, data retrieval of patients is simplified and sped up making it easier to access medical information.
NLP-powered chatbots can provide customer service by answering routine questions and handling simple requests. A lot of Industry analysts predict that Chatbots are an emergent trend which will offer real-time solutions for simple customer service problems.