Applications in Healthcare – Fundamentals of Natural Language Processing
Applications in Healthcare
Doctors may instantly convert ideas into medical records using speech recognition software. Deaf people can understand what is being said by employing closed captions and speech recognition software to translate spoken words into text. People who have trouble using their hands to type on keyboards can use speech recognition to interact with computers.
Reporting in Court
To transcribe courtroom proceedings, software can be used, eliminating the need for human transcribers.
Recognizing Emotions
This device may examine particular voice traits to ascertain the speaker’s emotional state. This can be used in conjunction with sentiment analysis to determine how a customer feels about a product or service.
Hands-Free Communication Is Possible
Voice control lets people use phones, radios, and global positioning systems without using their hands.
Machine Translation
International and cross-cultural teamwork is often necessary for success, and you need to be able to get past language barriers to do this. AI can be used to automate the translation of written and spoken languages. For example, an inbox add-in could be used to automatically translate emails that come in or go out, or a conference call presentation system could show a transcript of the speaker’s words in different languages at the same time.
Machine translation (MT) is a type of automated translation in which software is used to translate a text from one natural language (like English) to another (like Spanish) while keeping the meaning of the original text. For any translation, whether it’s done by a person or by a computer, to work, the meaning of a text in its original (source) language must be completely recreated in the target (or translated) language. While it appears simple on the surface, it is significantly more complicated. A translator application must look at and think about every part of the text and know how each word affects the whole. To do this, you need to have a solid grasp of grammar, syntax (how sentences are put together), semantics (what they mean), and other parts of the source and target languages, as well as each local area. Both human and machine translations present their own challenges. For example, it’s unlikely that two human translators will come up with the same translation of the same text in the same language pair, and it may take more than one try to make the customer happy.