Natural Language Processing (NLP) is a branch of artificial intelligence mainly used for interactions between computers and human language. The fundamental objective of NLP is to read, decipher, understand, and make sense of human languages in a manner that is beneficial. Most NLP techniques and machines are dependent on each other to interpret a clear meaning from human languages. Interactions between humans and machines using Natural Language Processing could go as follows in certain manners like

1. A human talks to the machine.
2. The machine captures the audio.
3. audio-to-text conversion takes place.
4. Processing of the text’s data.
5. Data to audio conversion takes place.
6. The machine responds to the human by playing the audio file.

Natural Language Processing is a common medium between us and the applications we use in our day-to-day lives. Applications such as Google Translate or basically any other language translation app, Word Processors such as Microsoft Word and Grammarly that utilize NLP to detect grammatical errors, Interactive Voice Response (IVR) applications used in call centers to respond to certain users’ requests, and Personal Assistant applications such as OK Google, Siri, Cortana, and Alexa. 

Another complex example of Natural Language Processing is Text-to-speech (TTS). The principal objective of TTS is to read out digital texts, take words on a computer or other digital device and convert them into audio. TTS is extremely beneficial for kids struggling with dyslexia. Text-to-speech is the driving force behind YouTube and Twitch, apps that make use of the technology. Streamers on both platforms get donations from viewers with the help of technology.

Sometimes, the complicated human nature makes it difficult for NLP-based machines to decrypt and understand the intended message. The output can be fruitful and disappointing simultaneously. Ambiguity, improper use of words, and impreciseness can lead to complications and be a barrier to the implementation of data.

Natural Language Processing uses Syntactic analysis and semantic analysis to complete tasks.  Here is a complete description of how they can be used:

1. Syntax: Syntax is the set of rules, principles, and processes that govern the structure of sentences in a given language, usually including word order. The term syntax is also used to refer to the study of such principles and processes. In NLP, syntactic analysis can be used to derive meanings from a group of words by applying grammatical rules with the help of computer algorithms.
2. Semantics: Semantics is the study of meaning, reference, or truth. The term can be used to refer to subfields of several distinct disciplines, including philosophy, linguistics, and computer science. In NLP, it involves applying computer algorithms to understand the meaning and interpretation of words and how sentences are structured.

With the ever-increasing rise of technology, we expect more usage of NLP in different fields. The future truly holds something truly marvelous.